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Sensor Search Based on Sensor Similarity Computing in the Internet of Things
Suyan LIU, Yuanan LIU, fan WU, Wenhao FAN
, doi: 10.11999/JEIT171085
[Abstract](0) [FullText HTML](0) [PDF 1760KB](0)
The Internet of Things (IoT) is becoming a hot research area, and tens of billions of devices are being connected to the Internet which are advancing on the sensor search service. IoT features (searches are strong spatiotemporal variability, limited resources of the sensor, and mass heterogeneous dynamic data) raise a challenge to the search engines for efficiently and effectively searching and selecting the sensors. In this paper, Piecewise-Linear fitting Sensor Similarity (PLSS) search method is proposed. Based on the content values, PLSS calculates the sensor similarity models to search most similarity sensors. PLSS improves the accuracy and efficiency of search compared with FUZZY set algorithm (FUZZY) and least squares method. PLSS storage costs are at least two order of magnitude less than raw data.
Consistency Enhancement Quality Assessment Criterion in Confidence Interval for Image Set
Hao LIU, Xiaofan SUN, Xinsheng ZHANG, Leming WU, Qigang KUANG
, doi: 10.11999/JEIT180272
[Abstract](7) [FullText HTML](10) [PDF 3389KB](4)
When evaluating the enhancement quality of a whole image set, the existing average score criterion will vary inconsistently with different image sets and produce a large evaluation quality fluctuation. Therefore, this paper proposes a consistency enhancement quality assessment criterion in confidence interval for any image set. By setting application parameters and using confidence interval to screen data, the proposed criterion compares the quality score difference before and after enhancing each image, and evaluates the consistency of image quality enhancement, and then calculates the effective value of consistency enhancement quality scores. Among many image enhancement algorithms, the proposed criterion can select the high-reliability enhancement algorithm for a specific application. The experimental results show that the proposed criterion has good subjective and objective consistency and outperforms the existing average score criterion, which provides an evaluation criterion for those image enhancement algorithms applied to any image set.
Analysis of FM Signals Based on Viterbi. SynchroSqueezing S-transform in Mixture Noise
Mingzhe ZHU, Rui XIAO, Xiaofan SU, Guanghui WANG
, doi: 10.11999/JEIT171091
[Abstract](7) [FullText HTML](6) [PDF 1559KB](3)
A new robust Generalized Synchrosqueezing S-Transform(GSST) is proposed to solve the distortion problem of SynchroSqueezing S-Transform(SSST) in mixture noise. Firstly, the method improves the Viterbi algorithm for improving the Time-Frequency(TF) analysis performance of S-transform in alpha-gaussian mixture noise. After acquiring the phase locus information of the FM signal, the synchrosqueezing is used to improve the time-frequency aggregation. The simulation results show that the proposed method can accurately obtain the time-frequency information of FM signal under the background of Alpha-Gaussian mixture noise in low SNR, and has a better robustness and applicability than the SST.
Improved Metric Learning Algorithm for Person Re-identification Based on Equidistance
Zhiheng ZHOU, Kaiyi LIU, Junchu HUANG, Zengqun CHEN
, doi: 10.11999/JEIT180336
[Abstract](7) [FullText HTML](6) [PDF 1634KB](0)
In order to improve the robustness of MLAPG algorithm, a person re-identification algorithm, called Equid-MLAPG algorithm, is proposed which is based on the equidistance measurement learning strategy. Due to the imbalanced distribution of positive and negative sample pairs in the mapping space, sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs. Therefore, Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space. That is, the distance of a positive sample pair in the transform space is mapped to be zero, resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences. Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.
Noise Suppression Algorithm for Ocean Magnetic Anomaly Detection
Chunjiao FEI, Qunying ZHANG, Peilin WU, Guangyou FANG, Wanhua ZHU, Xin XU
, doi: 10.11999/JEIT180026
[Abstract](167) [FullText HTML](105) [PDF 2814KB](26)
Marine magnetic anomaly detection is one of the basic means of marine scientific observation, exploration of undersea resources, national defense and security. However, the complexity of the magnetic field noise increases the difficulty of the magnetic detection. It is of great significance to study various magnetic field noise mechanisms and suppression methods for the improvement of measurement accuracy. In this paper, the wave magnetic field model under general and infinite depth conditions is used to estimate the noise induced by sea waves respectively. The wave and geomagnetic noise in the magnetic anomaly signal is filtered out by the combination of spectral subtraction and wavelet. In order to verify the validity of the algorithm, the ocean magnetic field in a sea area of South China Sea in August 2015 is observed. The results show that this method can filter out most of the wave and geomagnetic field noise. The wave distribution in the frequency range of 0.4~0.8 Hz is obviously reduced, the waveform in the time domain is greatly improved, the magnetic anomaly signal of the target is highlighted. Signal to noise ratio can be increased by nearly 11 dB. The proposed method has the advantages of low computational complexity, strong real-time performance and easy implementation, which can provide an effective measure for noise suppression of marine magnetic anomaly detection.
Neighbor Discovery Mechanism for Underwater Acoustic Communication Networks Based on Directional Transmission and Reception
Jianmin YANG, Gang QIAO, Donghu NIE, Lu MA
, doi: 10.11999/JEIT180108
[Abstract](180) [FullText HTML](104) [PDF 1636KB](12)
Considering the difficulty of neighbor discovery in underwater acoustic communication networks, a neighbor discovery mechanism is presented based on directional transmission and reception. In this mechanism, the nodes only send and receive signals directionally, which can avoid the hidden terminal problem caused by asymmetric gain and increase the network coverage. Time is divided into neighbor discovery time slot and listening & reply time slot. In neighbor discovery time slot, the node sends the HELLO signal, and then waits to receive the REPLY signal sent by its neighbor node. In listening & reply time slot, the node listens the channel for the HELLO signal sent by the source node, then replies REPLY signal to the source node. The node can discover its neighbor through HELLO/REPLY two-way handshake based on competition and direct & indirect discovery, which can overcome the " deaf” nodes problem and improve the efficiency of neighbor discovery. Compared with the randomized two-way neighbor discovery mechanism, simulation tests show that the proposed mechanism has the shorter average discovery latency and the higher average discovery ratio in various network density and number of antenna sectors.
A Multi-mode Spread Doppler Clutter Suppression Algorithm of MIMO-OTHR Based on Bi-iterative MVDR Beamformer
Wenqi YU, Jianwen CHEN, Xue LI
, doi: 10.11999/JEIT180101
[Abstract](98) [FullText HTML](47) [PDF 1901KB](3)
Spread Doppler Clutter (SDC) caused by multi-mode propagation restrains the detection performance of Over-The-Horizon Radar (OTHR) for low detectable targets, such as slow ships. To solve this problem, a bi-iterative Minimum Variance Distortionless Response (MVDR) beamformer is proposed to suppress multi-mode SDC for MIMO OTHR system. As it is difficult to obtain the signal-free training data and enough sample support in MIMO-OTHR with time-staggered linear frequency modulated continuous wave or slow time phase-coded waveforms, the block matrix is used for data preprocessing to reduce the effect of expected signal component in the training data, then multi-mode SDC could be suppressed by the LN-variate MVDR beamformer which is restored through bi-iterative calculation with an L-variate transmit and an N-variate receive beamformer. This algorithm improves the convergence of MVDR beamformer, while reducing the computational load and the requirement of sample support. Theoretical analysis and simulation experiment are presented to verify the effectiveness of this algorithm.
Performance Analyze for Multiuser-DCSK Communication System Based on Hilbert Transform
Gang ZHANG, Jiaping XU, Tianqi ZHANG
, doi: 10.11999/JEIT180110
[Abstract](118) [FullText HTML](70) [PDF 2200KB](11)
A multiuser Differential Chaos Shift Keying (DCSK) communication system based on Hilbert transform is proposed (HMU-DCSK), to solve the problem of low transmission rate of DCSK. Under the condition of fixed-order Walsh codes, the set of orthogonally-based signals is doubled by the Hilbert transform and the carrier signals assigned to each user are guaranteed to be orthogonal. The Bit-Error-Rate (BER) formula in Rayleigh fading channel is derived and numerous simulations are conducted. The simulation results show that the transmission rate of HMU-DCSK system is twice that of traditional multiuser DCSK system under the same N value, meanwhile, the BER performance of HMU-DCSK system is obviously better than the traditional multi-user DCSK system under the same transmission rate.
Random Chirp Frequency-stepped Signal ISAR Imaging Algorithm Based on Joint Block-sparse Model
Mingjiu LÜ, Wenfeng CHEN, Saiqiang XIA, Jun YANG, Xiaoyan MA
, doi: 10.11999/JEIT180054
[Abstract](116) [FullText HTML](69) [PDF 1814KB](17)
Under the condition of lack of echo data and low SNR, the ISAR imaging performance greatly reduced by using Random Chirp Frequency-Stepped (RCFS) signal. To solve the above problems, based on fully analyzing the echo characteristics of the random chirp frequency-stepped signal, a new method of obtaining high quality ISAR images is proposed using the joint sparse feature of the target range dimension. First, a joint block sparse imaging model of the target echo signal under the condition of random chirp frequency-stepped signal is derived and the characteristics of the model are analyzed. Secondly, a Joint Block sparse Orthogonal Matching Pursuit (JBOMP) algorithm is proposed for solving the model. The algorithm utilizes the sparse information and the joint sparse information of the ISAR echo. Therefore, the ISAR imaging performance is enhanced under the condition of low measurement and low SNR. The proposed algorithm also can achieve joint processing of multidimensional signals and has a faster operation speed. Both theoretical analysis and simulation experiments verify the effectiveness of the proposed method.
Accurate Acquisition of High Order Double Binary Offset Carrier Signals for High Dynamic Environment
Tianqi ZHANG, Shuai YUAN, Donghua LIU, Qun LI
, doi: 10.11999/JEIT180087
[Abstract](86) [FullText HTML](58) [PDF 2652KB](13)
For the problem of without accurate acquisition of Double Binary Offset Carrier (DBOC) modulated signal for high dynamic environment, a method which is based on Partial Matched Filtering (PMF) - Fast Fourier Transform (FFT) is proposed. According to the problem of low detection performance caused by the related loss and scallop loss, a new improved acquisition scheme is proposed. Firstly, the Discrete Polynomial phase Transform (DPT) is used to remove the high order dynamic term of the received signal, and then the PMF-FFT algorithm is redesigned for the DBOC signal. Finally, the spectrum correction method is used to correct the maximum power spectrum after FFT. Simulation results show that, under the same conditions, the proposed scheme improves the detection probability by about 2 dB, and shortens effectively the acquisition time.
Matrix Pencil Method Based Processing Approach for Spaceborne MEB SAR with Digital Beamforming in Elevation
Kai YE, Weidong YU, Wei WANG
, doi: 10.11999/JEIT180076
[Abstract](108) [FullText HTML](37) [PDF 2001KB](8)
Digital BeamForming (DBF) in elevation plays a crucial role for spaceborne Multiple Elevation Beam (MEB) SAR realizing the High-Resolution Wide-Swath (HRWS) imaging mode. However, due to the influence of satellite attitude error, the deviation of the DBF receiving beam direction always arises in such system. This leads to ghost targets appearing in the SAR image, when mapping the scenes (such as the seaport areas) with strong scatterers. To address the problem, a matrix pencil method based DBF processing approach in elevation is presented. Firstly, according to the given threshold, the peak position of the strong scatterer is found from the range-compressed signals. Then, the direction of arrival angle of the strong scatterer is estimated using the matrix pencil method. Finally, based on the imaging geometry model, the DBF weighting vector is adjusted to ensure the receiving beam to precisely point to the signal sources. Thereby, the interferences of ghost targets in SAR image can be eliminated effectively. The theoretical analysis is derived in detail, then it is validated by simulation experiments.
Research on Secret Sharing Scheme Without Trusted Center Based on Eigenvalue
Yanshuo ZHANG, Wenjing LI, Geng ZHAO, Qingrui WANG, Wei BI, Tao YANG
, doi: 10.11999/JEIT180197
[Abstract](172) [FullText HTML](91) [PDF 441KB](20)
By using the characteristic of matrix eigenvalues, this paper proposes a new secret sharing scheme without trusted center. The scheme does not require a trusted center,and each participant provides the same secret share (column vector) and generates its own secret share in the black box, thus avoiding the authority deception of the trusted center. Reversible matrix P consisting of column vectors provided by all participants,and diagonal matrix \begin{document}${Λ}$\end{document} generate a matrix M. Then, the orthogonalized unit eigenvectors of the matrix M is distributed to each participant as a subkey.Because the eigenvalues corresponding to the participants in the same set are the same, this scheme can effectively prevent malicious fraud among members.Analysis results show that the program is feasible and safe.
Aircraft Detection Method Based on Deep Convolutional Neural Network for Remote Sensing Images
Zhi GUO, Ping SONG, Yi ZHANG, Menglong YAN, Xian SUN, Hao SUN
, doi: 10.11999/JEIT180117
[Abstract](187) [FullText HTML](103) [PDF 8073KB](47)
Aircraft detection is a hot issue in the field of remote sensing image analysis. There exist many problems in current detection methods, such as complex detection procedure, low accuracy in complex background and dense aircraft area. To solve these problems, an end-to-end aircraft detection method named MDSSD is proposed in this paper. Based on Single Shot multibox Detector (SSD), a Densely connected convolutional Network (DenseNet) is used as the base network to extract features for its powerful ability in feature extraction, then an extra sub-network consisting of several feature layers is appended to detect and locate aircrafts. In order to locate aircrafts of various scales more accurately, a series of aspect ratios of default boxes are set to better match aircraft shapes and combine predictions deduced from feature maps of different layers. The method is more brief and efficient than methods that require object proposals, because it eliminates proposal generation completely and encapsulates all computation in a single network. Experiments demonstrate that this approach achieves better performance in many complex scenes.
New Back-filtering PFA Imaging Algorithm and Distortion Correction Method for Missile-borne Bistatic SAR with Curved Track
Huan DENG, Yachao LI, Haiwen MEI, Yinghui QUAN, Mengdao XING
, doi: 10.11999/JEIT170994
[Abstract](9) [FullText HTML](9) [PDF 2130KB](1)
The traditional bistatic equivalent range model has low accuracy and make the traditional Polar Format Algorithm (PFA) are inapplicable in missile-borne bistatic Synthetic Aperture Radar (SAR) imaging with curved track due to the existing of three-axis velocity and acceleration. In addition, due to the existing of space-variant motion error introduced by acceleration, the traditional 2-D sub-block compensation method will cause the discontinuities between the image sub-blocks, and thus affecting the subsequent image matching application. In view of these problems, this paper proposes a Back-Filtering PFA algorithm (BFPFA) which is based on the Improved Generalized Bistatic Equivalent Range Model (IGBERM). Constructing the combined compensating filter of space-variant phase error and geometric distortion, as well as reverse mapping interpolation, can realize the combined compensation of motion error, wavefront bending and geometric distortion in the process of oblique conversion, and obtain the SAR distance map without distortion, which is more conducive to the subsequent image matching applications. Finally, the simulations validate the effectiveness of the proposed algorithm.
User Identification Across Social Networks Based on User Trajectory
Hongchang CHEN, Qian XU, Ruiyang HUANG, Xiaotao CHENG, Zheng WU
, doi: 10.11999/JEIT180130
[Abstract](199) [FullText HTML](85) [PDF 631KB](29)
The performance of trajectory based user identification is poor since the existing methods ignore the order feature of location sequence. To solve this problem, a Cross Domain Trajectory matching algorithm based on Paragraph2vec (CDTraj2vec) is proposed. Firstly, the user trajectory is transformed to the grid representation which is easy to handle. And the PV-DM model in the Paragraph2vec algorithm is utilized for extracting order feature of location sequence in trajectory. Then the original user trajectories are divided by a certain time size and distance scale to construct a training sample suitable for training PV-DM model. The PV-DM model is trained by different types of training samples, and the vector representation of the user trajectories is obtained. Finally, the matching of the trajectory is determined by the user trajectory vector. Experimental results on BrightKite shows that the F-measure is improved by 2%~4% compared with the existing frequency based and distance based algorithm. The proposed algorithm can effectively extract the order feature of location sequence, and realize the trajectory based user identification across social networks.
Two-dimension Space-variance Correction Approach for Diving Highly Squinted SAR Imaging with Sub-aperture
Yanfeng DANG, Yi LIANG, Bowen BIE, Jinshan DING, Yuhong ZHANG
, doi: 10.11999/JEIT180021
[Abstract](110) [FullText HTML](43) [PDF 2559KB](4)
The diving SAR usually adopts the highly squinted mode and sub-aperture to satisfy the maneuvering and real-time processing. However, the existence of severe range-azimuth coupling, range-dependent squint angle and three-dimension velocity and acceleration leads to the space variance of range envelope and azimuth phase, which makes imagery unfocused seriously. To solve these problems, a Two-stage Frequency Filtering Algorithm (TsFFA) is proposed. After preprocessing, the First-stage Frequency Filtering (FsFF) factor is first introduced to correct azimuth-dependent Range Cell Migration (RCM) and realize the unified RCM correction. Furthermore, the Second-stage Frequency Filtering (SsFF) factor is adopted to equalize azimuth-dependent Doppler parameters and realize unified azimuth phase focused. Simulation results are presented to validate the effectiveness of the proposed approach.
Non-Invasive Wireless and Passive MEMS Intraocular Pressure Sensor
Junbo WANG, Chaochao HE, Deyong CHEN, Jian CHEN, Qiuxu WEI
, doi: 10.11999/JEIT180045
[Abstract](180) [FullText HTML](52) [PDF 2434KB](6)
Continuous monitoring of IntraOcular Pressure (IOP) plays an important role in the diagnosis and treatment of the glaucoma. Existing IOP sensors have some problems, such as low sensitivities, high central resonant frequencies and difficult fabrication. In order to solve the aforementioned problems, this paper presents a wireless, passive and non-invasive IOP sensor based on MEMS technology. The sensor contains five stacked layers, where Parylene, copper and PDMS are adopted as the functional materials within two flexible substrate layers, two electrode layers, and a dielectric layer, respectively. The electrode layers and the dielectric layer consist of two inductors and two capacitors to form a resonant circuit in C-L-C-L series. In the term of fabrication, a MEMS planar process followed by thermally shaping is proposed to fit curved surfaces of the eyeballs, and then this design scheme can effectively solve such issues as the difficulty in making the sensor and so on. Experimental results show that the central resonant frequency is decreased to 40 MHz, relative sensitivity is quantified as 1028.57 ppm/kPa, and resolution reached up to 50 Pa (0.375 mmHg). This study can be used for long-term, continuous monitoring of IOP.
Airborne Bistatic Radar Clutter Suppression Based on Sparse Bayesian Learning
Xiaode LÜ, Jingmao YANG, Qi YUE, Hanliang ZHANG
, doi: 10.11999/JEIT180062
[Abstract](14) [FullText HTML](17) [PDF 1809KB](4)
Clutter of airborne bistatic radar is related to configuration and has serious range dependence characteristic, therefore the clutter ridge is complex and variable, and few Independent and Identically Distributed (IID) samples exist. As the result, the traditional Space-Time Adaptive Processing (STAP) has a degraded suppression performance for airborne bistatic radar clutter. Based on the sparsity of airborne radar clutter in the angle-Doppler domain and the advantages of Sparse Bayesian Learning (SBL) in sparse signal reconstruction, SBL algorithm is applied to the more complex airborne bistatic radar with both transmitter and receiver moving. The method can estimate the Clutter Covariance Matrix (CCM) of the unit under test with very few training samples, then perform space-time adaptive processing. Since the method does not need independent and identically distributed samples, it has better performance of clutter suppression in the airborne bistatic radar with both transmitter and receiver moving. Simulation results verify the effectiveness of the algorithm.
Fast High-resolution Imaging Method for Wideband Spinning Targets Under Sub-Nyquist Sampling
Hu XIANG, Shaodong LI, Long XIANG, Wenfeng CHEN, Jun YANG
, doi: 10.11999/JEIT180099
[Abstract](97) [FullText HTML](13) [PDF 1523KB](3)
When using Inverse Synthetic Aperture Radar (ISAR) to observe the spinning targets, the range-Doppler time-varying characteristics of spinning target echo would lead to the inefficiency of traditional imaging methods. To solve this problem, a fast high-resolution imaging method based on distributed matching sparse representation model is proposed for wideband spinning targets imaging. Firstly, a distributed matching sparse representation model is constructed based on the sparsity of spinning target echo. Secondly, a Fast Distributed Simultaneous Multiple Orthogonal Matching Pursuit (FDSMOMP) algorithm is proposed for achieving the fast robust imaging of the spinning parts. The proposed algorithm can significantly improve the reconstruction efficiency by reducing the iteration times and computational complexity of each iteration. Additionally, in order to enhance the robustness of FDSMOMP, a related threshold is designed to suppress the false reconstruction. Finally, the mechanism of the presented method is analyzed theoretically, and it is proved that the high quality imaging result can still be obtained under the conditions of sub-Nyquist sampling and lower SNR (Signal Noise Ratio). Simulation results show the validation of the proposed method.
Researches on Pseudo-range Biases of BeiDou Navigation Satellite System B1 Signals
Chengyan HE, Ji GUO, Xiaochun LU, Xue WANG, Yongnan RAO, Li KANG, Meng WANG
, doi: 10.11999/JEIT180074
[Abstract](155) [FullText HTML](79) [PDF 1575KB](20)
Due to the distortions of the broadcasted satellite signals and the inconsistencies of parameter settings for different receivers, the single difference or double difference of pseudo-ranges between two receivers are different for two pair of different receivers. Bias inconsistencies will lead to adverse effects for pseudo-range-based positioning applications. Pseudo-range biases can also hinder carrier-phase ambiguity resolution. However, fewer articles deal with pseudo-range biases for BeiDou navigation satellite System (BDS). In order to mitigate the impact of biases on BDS to the greatest extent, the generation mechanisms and characteristics of pseudo-range biases are studied in detail firstly. Then based on this, experimental verification methods are designed using Haoping Radio Observatory (HRO) of Chinese Academy of Sciences to observe BDS signals. Pseudo-range biases of all visible BDS satellites are measured and evaluated with high accuracy, using the 40 meters dish antenna and modern equipment of HRO. Finally, some important parameters of BDS receivers, such as the correlator spacing and front-end bandwidth, are suggested to mitigate the ranging errors and positioning errors result from pseudo-range biases. The achievements of this paper can provide a worthy reference for GNSS signal designers, GNSS monitoring and assessment and GNSS receiver designers.
Design and Verification of Monolithic Integrated SAR System
Manlai DING, Xingdong LIANG, Li TANG, Zhilei WENG, Yixiao WANG
, doi: 10.11999/JEIT171203
[Abstract](12) [FullText HTML](14) [PDF 3594KB](4)
The micro Synthetic Aperture Radar (SAR) system based on the traditional GaAs and GaN devices is not conducive to the monolithic integration, and the development bottleneck of volume, power consumption, weight and cost is becoming increasingly apparent, which is impossible to meet the needs of the miniaturized and ubiquitous unmanned platforms in the future. A new scheme for the design of a fully coherent Frequency Modulated Continuous Wave (FMCW) SAR with high resolution is proposed. The design method of high pulse phase stability and high isolation is studied and realized. The prototype of micro SAR is developed based on silicon chip and experimentally demonstrated. The micro SAR operates at K band, producing a signal bandwidth of wider than 2 GHz, enabling a range resolution of 7.5 cm. The system has made remarkable progress in terms of size, weight, power consumption and lay technical foundation for the monolithic integration of micro SAR system in a silicon chip.
Energy Efficient Joint Power Allocation and Beamforming for Cloud Radio Access Network
Jiakuo ZUO, Longxiang YANG, Nan BAO, Guanming LU
, doi: 10.11999/JEIT180218
[Abstract](9) [FullText HTML](5) [PDF 1053KB](0)
The resource allocation for Cloud Radio Access Network (C-RAN) is investigated. The max-min fairness criterion is used as the optimization criterion and the Energy Efficiency (EE) of C-RAN users is taken as the optimization objective function, by maximizing the EE of the worst link under the constraints of maximum transmit power and minimum transmit rate, the user transmit power and Remote Radio Heads (RRHs) beamforming vectors are jointly optimized. The above optimization problem belongs to the nonlinear and fractional programming problem. First, the original nonconvex optimization problem is transformed into an equivalent optimization problem in subtractive form. Then, by introducing a new variable, non-smooth equivalent optimization problem is transformed into a smooth optimization problem. Finally, a two-layer iterative power allocation and beamforming algorithm is proposed. The proposed algorithm is compared with traditional non-EE resource allocation algorithm and EE maximization algorithm. The experimental results show that the proposed algorithm is effective in improving the EE and the fairness of resource allocation.
A Hierarchical Foreign Object Debris Detection Method Using Millimeter Wave Radar
Baoshuai WANG, Zhu LAN, Zhengjie LI, Xiaobin WANG, Hongtao HU
, doi: 10.11999/JEIT180200
[Abstract](9) [FullText HTML](9) [PDF 3361KB](3)
Detection of stationary little targets in heavy ground clutter is the key problem facing the millimeter wave airport runway Foreign Object Debris (FOD) detection radar. This paper proposes a hierarchical FOD detection algorithm based on power spectrum feature extraction and Support Vector Domain Description (SVDD) classifier. The clutter map Constant False Alarm Rate (CFAR) detection algorithm is first utilized to suppress the complex background clutter. In order to solve the high false alarm problem after the clutter suppression, the power spectrum features are extracted to transform the radar returns into the feature domain where the FOD and false alarm are more distinguishable. Finally, the one-class SVDD classifier is utilized to categorize the FOD and false alarm into different kinds so as to reduce the false alarm rate. Experimental results based on measured data show that the proposed method can achieve good detection performance.
Flow Caching in Protocol Oblivious Forwarding Switches
Zuowei CAO, Xiao CHEN, Hong NI, Xiaozhou YE
, doi: 10.11999/JEIT180042
[Abstract](87) [FullText HTML](11) [PDF 1284KB](1)
Protocol Oblivious Forwarding (POF) supports the arbitrary protocol processing, enhancing the programmability of Software Defined Networking (SDN). In order to improve the forwarding performance, a flow caching method is proposed. To parse the packet in advance, absolute positions of matching fields are obtained by identifying the dependency of matching and actions. To guarantee the acceleration effect of flow caching, flow tables are selected according to their matching types and number of entries. In addition, the single-flow table cache and multi-flow table cache are compared and an adaptive switching strategy is proposed based on the actual situation of network traffic. The POFSwitch is extended to implement the proposed method and it is validated under the real rules and backbone traces. The switch packet forwarding rate is increased by 220% after applying flow caching. Flow caching can provide higher forwarding performance for programmable data planes.
Fast Multi-objective Antenna Design Based on Improved Back Propagation Neural Network Surrogate Model
Jian DONG, Wenwen QIN, Yingjuan LI, Qianqian LI, Lianwen DENG
, doi: 10.11999/JEIT180025
[Abstract](176) [FullText HTML](105) [PDF 1794KB](22)
Focusing on the problem of reducing the large computation cost of traditional antenna design methods, a new surrogate model based on Back Propagation Neural Networks (BPNN) is constructed. In order to solve the problem of easily falling into local optimum in BPNN, a PSO-BPNN surrogate model is developed by improving initial structural parameters of neural networks and applied to fast multi-objective optimization design of multi-parameter antenna structures. The design results show that the proposed PSO-BPNN outperforms other existing antenna surrogate models in terms of prediction accuracy and prediction speed. The proposed method is of value in dealing with complex antenna designs with high-dimensional parameter space.
Direction Finding Using Scanned Beams Based on Matched Reconstruction of Antenna Pattern
Xiaodan ZHU, Weiqiang ZHU, Zhuo CHEN
, doi: 10.11999/JEIT180002
[Abstract](7) [FullText HTML](7) [PDF 2083KB](1)
Estimation of Direction Of Arrival (DOA) with scanned beams of single rotational antenna is meaningful. To obtain precise estimation with low computation burden, a closed-form estimator is proposed based on estimating the mode component. Firstly, the problem can be transformed into the estimation of mode component when antenna pattern is expressed with a formula of exponential sums, thus DOA can be induced from each mode. Considering the estimation error, a multi-mode estimator with its theoretical error is derived. Non-ideal observing conditions result in an ill-determined problem for the estimation of mode component. A modified method is proposed by reconstructing the antenna pattern. By calculating cross-correlation of the observed amplitude trains with the antenna pattern samples, a coarse estimation of DOA is obtained to determine the angle range under the matched reconstruction. Then, ill-determined problem can be avoided if the converted mode component is calculated with the new pattern. Both theoretical and simulation results demonstrate that the proposed method can obtain high precise estimation with low computation cost, and the proposed matched reconstruction approach extends the adaptability of the method.
Threat Assessment Based Sensor Control for Multi-target Tracking
Hui CHEN, Zhongliang HE, Feng LIAN, Chen LI
, doi: 10.11999/JEIT180212
[Abstract](14) [FullText HTML](8) [PDF 1460KB](0)
This paper proposes a threat assessment based sensor control by using multi-target filter with random finite set. First, the general sensor control approach based on information theory is presented in the framework of Partially Observable Markov Decision Process (POMDP). Meanwhile, combined with target movement situation, the factors that affect the target threat degree are analyzed. Then, the multi-target state is estimated based on the particle multi-target filter, the multi-target threat level is established according to the multi-target motion situation, and the maximum threat target distribution characteristic is analyzed and extracted from the multi-target distribution characteristic. Finally, the Rényi divergence is used as the evaluation index in sensor control, and the final control policy is solved with the maximum information gain as the criterion. The simulation results verify the feasibility and effectiveness of the proposed method.
V2X Task Offloading Scheme Based on Mobile Edge Computing
Haibo Zhang, Qiuji Luan, Jiang Zhu, Xiaofan He
, doi: 10.11999/JEIT180027
[Abstract](125) [FullText HTML](46) [PDF 1427KB](10)
Mobile Edge Computing (MEC) draws much attention in the next generation of mobile networks with high bandwidth and low latency by enabling the IT and cloud computation capacity at the Radio Access Network (RAN). Matching problem between requesting nodes and servicing nodes is studied when a vehicle wants to offload tasks, a MEC-based offloading framework in vehicular networks is proposed, Vehicle can either offload task to MEC sever as V2I link or neighboring vehicle as V2V link. Taking into account the limited and heterogeneous resources, and the diversity of tasks, offloading framework is established as combination auction model, and a multi-round sequential combination auction mechanism is proposed, which consists of Analytic Hierarchy Process (AHP) ranking, task bidding and winners decision. Simulation results show that the proposed mechanism can maximize the efficiency of service nodes while increasing the efficiency of requesting vehicles under the constraints of the delay and the capacity.
A Novel Broadband Circularly Polarized Monopole Antenna
Zhenya LI, Xiaosong ZHU, Jianhua ZHANG
, doi: 10.11999/JEIT180094
[Abstract](97) [FullText HTML](48) [PDF 4557KB](4)
A novel broadband circularly polarized monopole antenna is proposed by microstrip feed line. The antenna is composed of C-shaped patch and an improved ground plane with the overall size of 25×25×1 mm3. The impedance bandwidth and axial ratio bandwidth of the antenna can be effectively widened by cutting the corner on the C-shaped patch and adding triangular stubs on the ground plane. The design procedure of the antenna is given, and the working mechanism of the circularly polarized antenna is analyzed from the surface current distributions. Besides, the antenna is fabricated and measured. Simulated and measured results show that the antenna has ultra wide impedance bandwidth and axial ratio bandwidth. The operating bandwidth of the antenna is 4.35~12 GHz (relative bandwidth 93.6%), and the 3 dB axial ratio bandwidth is 4.15~11.8 GHz (relative bandwidth 95.9%). At the same time, the radiation performance and gain characteristics of the antenna are measured and the measured results are in good agreement with the simulated results, which proves the effectiveness of the antenna. The antenna can be applied to Ultra-WideBand (UWB) wireless communication systems and satellite communication systems.
Timing Synchronization Method for Continuous Phase Frequency Shift Keying Signal Based on Multi-symbol Detection
Tian LIU, Song MA, Tian YUAN, Shihai SHAO
, doi: 10.11999/JEIT180060
[Abstract](107) [FullText HTML](70) [PDF 1971KB](4)
Continuous Phase Frequency Shift Keying (CPFSK) is widely adopted as a standard by the telemetry community. The Multi-Symbol Detection (MSD) technique can increase channel gain for the CPFSK telemetry system. Therefore, the timing synchronization method for CPFSK signal needs to adapt to the scenario with lower SNR. According to existing timing synchronization methods’ poor performance in low SNR, a novel timing synchronization method for CPFSK signal based on MSD is proposed, which is suitable to variable rate. The simulation results show that, when Eb/N0 is 0 dB and symbol rate is 2 Mbps, the proposed method achieves 2 dB more channel gain than the single symbol likelihood decision method, and has similar performance to the early-late gate code synchronization method with reduced hardware resource by 60%. Finally, the validity of the numerical simulation and resource evaluation is verified by principle prototype realization.
Resource Allocation for Wireless Powered Hybrid Multiple Access Networks
Guangchi ZHANG, Zhichao ZENG, Miao CUI, Fan LIN
, doi: 10.11999/JEIT180219
[Abstract](8) [FullText HTML](7) [PDF 1638KB](0)
Wireless powered technology is an effective way to extend the lifetime of wireless network nodes. A wireless powered hybrid multiple access system is studied that is consist of a base station and multiple users in clusters. The transmission of the system is divided into two phases. The base station broadcasts energy to the users in the first phase. The users transmit information to the base station in the second phase. The users among different clusters transmit in the time division multiple access manner, while the users in the same cluster transmit in the non-orthogonal multiple access manner. Joint phase time duration allocation and power allocation are investigated at the base station and the users in order to improve the spectrum efficiency and user fairness, respectively. Two algorithms are proposed, which maximize the system throughput and the minimum throughput of the clusters, respectively. Simulation results show that the two proposed algorithms can effectively increase spectral efficiency and guarantee fairness of user clusters, respectively.
Steering Vector and Covariance Matrix Joint Iterative Estimations for Robust Beamforming
Zhiwei YANG, Pan ZHANG, Ying CHEN, Huajian XU
, doi: 10.11999/JEIT180225
[Abstract](181) [FullText HTML](104) [PDF 1722KB](32)
Focusing on the problem of adaptive beamformer performance decreasing due to target steering vector constraint errors, an algorithm for robust beamforming with joint iterative estimations of steering vector and covariance matrix is proposed. First, the initial value of target steering vector is obtained by sparse reconstruction, following eliminating the target signal estimation in the sampling covariance matrix, the initialization of the covariance matrix is completed; Then, basing on the steering vector error optimization model, this algorithm adopts the convex optimization to estimate joint-iteratively target steering vector and interference plus noise covariance matrix. Finally, the adaptive weight vector is obtained with the steady estimations of steering vector and covariance matrix. Simulation results show output signal to interference and noise ratio is improved in the situation of target steering vector constraint errors.
A Deterministic Compressed Sensing Sampling Strategy for Diagnosis of Defective Array Elements Using Far-field Measurements
Wei LI, Weibo DENG, Qiang YANG, Marco Donald MIGLIORE
, doi: 10.11999/JEIT180175
[Abstract](9) [FullText HTML](10) [PDF 1022KB](2)
The structured random sampling strategy adopted in array diagnosis has negative influence on the performance of measurement matrix. Therefore, a compressed sensing based deterministic sampling strategy to diagnose defective array elements using far-field measurements is investigated in this paper. In the case of the number of failed elements satisfies sparsity, the sparse vector is constructed by subtracting incentives of reference array without failures and the array under test. Deterministic Partial Fourier Matrix (DPFM) is then formulated by the proposed strategy as the measurement matrix. Finally, accurate diagnosis with high probability is achieved by l1 norm minimization. Theoretical analysis and simulation results demonstrate that the proposed method can avoid the adverse impact on the performance of measurement matrix effectively arising from the random distribution of sampling positions, simplify the sampling procedure and improve the probability of success rate of diagnosis.
On Anti-outlier Localization for Integrated Long Baseline/Ultra-short Baseline Systems
Yan WANG, Qing LI, Guangpu ZHANG
, doi: 10.11999/JEIT180056
[Abstract](80) [FullText HTML](37) [PDF 952KB](4)
Complicated underwater environment puts forward high requirements on the fault-tolerant and reliability of underwater acoustic localization systems. An anti-outlier localization method based on K-Means Clustering and Decision Fusion (KMCDF) is proposed for integrated Long baseline/Ultra-Short BaseLine (L/USBL) systems. Firstly, the target position is preliminarily estimated by the multi-parameter redundant information measured by the integrated system. Then, the clustering degree of the preliminary coordinates is analyzed by k-means clustering. According to the incompatibility between outliers and normal parameters, the outliers are identified by the decision fusion method. Furthermore, the impact of outliers on positioning is eliminated. Simulation analysis shows that the proposed method fully incorporates the multi-parameter information, and the tolerance of outliers is better than the existing anti-outlier positioning methods based on the time-delay parameter. Lake trial results demonstrate further the effectiveness of the proposed method.
Single Channel Blind Separation Performance Bound of Non-cooperative Received Paired Carrier Multiple Access Mixed Signal
Yiming GUO, Hua PENG
, doi: 10.11999/JEIT180226
[Abstract](9) [FullText HTML](10) [PDF 2143KB](0)
Blind separation performance bound of Paired Carrier Multiple Access (PCMA) mixed signal is a measure of the separability of mixed signals and the performance of the separation algorithm. For the PCMA mixed signal, the spatial mapping of the modulation signal bits and symbols is constructed from the transmit signal model. The maximum likelihood criterion is used to derive the lower bound expression of separation performance independent of the separation algorithm. Numerical results agree well with the Viterbi simulation results under ideal conditions, which verify the rationality of the derived performance boundaries.
Aircraft Target Classification and Recognition Algorithm Based on Measured Data
Ming LI, Jiaojiao WU, Lei ZUO, Wanjie SONG, Huimin LIU
, doi: 10.11999/JEIT180024
[Abstract](153) [FullText HTML](100) [PDF 2086KB](37)
After analyzing the features of three measured data from the low-resolution radar system, corresponding to the helicopter, the propeller, and the turbojet, an algorithm is proposed by using multiple features to classify and recognize the aircraft targets. First, multiple features are extracted, including Doppler frequency shift, relative magnitude, waveform entropy of time and frequency domain, and time-frequency domain features from the measured data. Then, these features are utilized for classification purpose by means of the Support Vector Machine (SVM). Finally, owing to the symmetry and the width of time-frequency distributions of the returned signals between the helicopters with odd and even blades, a method is proposed to recognize of helicopter. The experimental results of measured data verify the effectivity of the proposed algorithms.
Multi-scale Face Detection Based on Single Neural Network
Hongzhe LIU, Shaopeng YANG, Jiazheng YUAN, Xueqiao WANG, Jianming XUE
, doi: 10.11999/JEIT180163
[Abstract](108) [FullText HTML](49) [PDF 2196KB](13)
Face detection is finding and locating all faces in the input image, and then returning the position and size of the faces. It is an important direction of target detection. In order to solve the problem which is caused by the diversity of face size, a new single shot multiscale face algorithm is presented based on feature fusion. This method combines predictions from multiple feature maps with different resolutions to handle faces of various sizes, and the fusion of the feature maps in the shallow layers can improve the detection accuracy of the small size face by introducing the contextual information. Experimental results on the FDDB and WIDERFACE datasets confirm that the proposed method has competitive accuracy. Additionally, the object proposal step is removed, which makes the method fast. The proposed model achieves 87.9%, 93.2% and 93.4% MAP (Mean Average Precision) on the WIDERFACE sub-datasets respectively, at 35 fps. The proposed method outperforms a comparable state-of-the-art HR model, and at the same time improves the speed while ensuring the accuracy.
Linear Complexity of Quaternary Sequences over Z4 Derived from Generalized Cyclotomic Classes Modulo 2p2
Xiaoni DU, Liping ZHAO, Lianhua WANG
, doi: 10.11999/JEIT180189
[Abstract](7) [FullText HTML](6) [PDF 392KB](0)
Based on the theory of Galois rings of characteristic 4, a new class of quaternary sequences with period 2p2 is established over Z4 using generated cyclotomy, where p is an odd prime. The linear complexity of the new sequences is determined. Results show that the sequences have larger linear complexity and resist the attack by Berlekamp-Massey (B-M) algorithm. It is a good sequence from the viewpoint of cryptography.
An Improved SSD Model for Saliency Object Detection
Chunyan YU, Xiaodan XU, Shijun ZHONG
, doi: 10.11999/JEIT180118
[Abstract](127) [FullText HTML](46) [PDF 2906KB](5)
Traditional saliency object detection methods, assuming that there is only one salient object, is not conductive to practical application. Their effects are dependent on saliency threshold. Object detection model provides a kind of new solutions. SSD can accurately detect multi-objects with different scales simultaneously, except for small objects. To overcome this drawback, this paper presents a new multi- saliency objects detection model, DAR-SSD, appending a deconvolution module embedded with an attention residual module. Experiments show that DAR-SSD achieves a higher detection accuracy than SOD. Also, it improves detection performance for multi- saliency objects on small scales, compared with original SSD, and it has an advantage over complicated background, compared with MDF and DCL, which also are deep model based methods.
A Coupled Non-local Total Variation Algorithm for Image Colorization
Zhengmeng JIN, Xiaowei LI, Tingting WU, Zhenzhen YANG
, doi: 10.11999/JEIT180141
[Abstract](93) [FullText HTML](31) [PDF 3774KB](1)
The traditional Total Variation (TV) model based on local operators for texture image colorization has some problems, such as inhomogeneous color diffusion, small coloring ranges and so on. In order to solve these problems, a coupled total variation model based on nonlocal operators is presented for image colorization, and the correspond numerical algorithm is designed to solve the model by incorporating the Alternating Direction Method of Multipliers (ADMM), and the convergence result of the algorithm is given. The proposed model makes full use of the similarity between the brightness of the pixel areas to perform color diffusion, which can effectively avoid the problem of inhomogeneous color diffusion due to local diffusion only using the brightness edge information. The experimental results are given to show that the model can effectively solve the problem of inhomogeneous color diffusion at textures and other details while fast colorizing.
Estimation of Unknown Line Spectrum Under Colored Noise via Sparse Reconstruction
Yilin WANG, Shilong MA, Jinjin WANG, Guolong LIANG, Qing LI
, doi: 10.11999/JEIT171040
[Abstract](11) [FullText HTML](16) [PDF 2165KB](5)
To solve the problem of the line spectrum estimation under colored noise background, a subband line spectrum estimation method using sparse reconstruction is proposed. Firstly, the input signal is divided into several subbands by a multi-rate cosine modulated filter bank. The subband signal has the flatter power spectrum. The sparse learning via iterative minimization method is utilized on each subband to estimate the line spectrum signal. Then, the results of line spectrum estimation on each subband are processed by frequency domain synthesis filtering and threshold decision. Finally, the line spectrum signal under colored noise background is identified. Theoretical derivation and simulation experiments show that the proposed method has better line spectrum estimation performance under colored noise background. The colored noise background can be removed, and the advantage of high frequency resolution of sparse reconstruction method is retained.
A Novel Method for Nonlinear Processing in Impulsive Noise Based on Gaussianization and Generalized Matching
Zhongtao LUO, Peng LU, Yangyong ZHANG, Gang ZHANG
, doi: 10.11999/JEIT180191
[Abstract](99) [FullText HTML](52) [PDF 1302KB](14)
A method based on Gaussianization and generalized matching, called Gaussianization-Generalized Matching (GGM) method is proposed, for nonlinear processing in impulsive noise. The GGM method can be designed based on noise samples, aided by nonparametric probability density estimation. Thus the GGM design is suitable for nonlinear processing in unknown noise models. The GGM method in the \begin{document}${\rm S\alpha S}$\end{document} model is analyzed, and also the comparison with another approach is presented based on unmatched noise model assumption in the Class A noise. The GGM method is applied to the constant false alarm rate technique via the efficacy function. Simulation and analysis results show that the GGM design is sub-optimal, works robustly when the noise model is unknown, and raises a low requirement on the sample number. Thus, the GGM method provides a promising choice when the noise model is unclear or time-varying.
Modified MUSIC Algorithm for Multiple Measurement Vector Models
Yun LIN, Qiang HU
, doi: 10.11999/JEIT180001
[Abstract](132) [FullText HTML](43) [PDF 1159KB](7)
The Compressed Sensing (CS) Multiple Measurement Vector (MMV) model is used to solve multiple snapshots problem with the same sparse structure. MUltiple SIgnal Classification (MUSIC) is a common method in traditional array signal processing applications. However, when the number of snapshots is below sparsity performance will be dramatically deteriorated. Kim et al. derive a modified MUSIC spectral method and propose a Compressed Sensing MUSIC method (CS-MUSIC) combining the compression reconstruction method and the MUSIC algorithm, which can effectively overcome the problem of insufficient snapshot number. In this paper, Kim et al.’s conclusion is extended to the general case, and a Modified MUSIC (MMUSIC) algorithm is proposed based on the traditional MUSIC method and the CS-MUSIC method. The simulation results show that the proposed algorithm can effectively overcome the shortage of snapshots and has a higher reconstruction probability than the CS-MUSIC algorithm and the compressed sensing greedy algorithm.
A Deep Metric Learning Based Video Classification Method
Hongxin ZHI, Hongtao YU, Shaomei LI, Chao GAO, Yanchuan WANG
, doi: 10.11999/JEIT171141
[Abstract](17) [FullText HTML](15) [PDF 1293KB](0)
To solve the common problem of classification performance restriction caused by big intra-class variations and inter-class similarities in video classification domain, this paper proposes a deep metric learning based video classification method. The proposed method designs a deep network which contains three parts: feature learning, deep metric learning based similarity measure as well as classification. The principle of similarity measure is: Firstly, the Euclidean distance between features is calculated as the semantic distance between samples. Secondly, a margin distributing function is designed to dynamically allocate margin in the basis of the semantic distances. Finally, the difference of the sample semantic distance can be learned by calculating the loss and propagating it backwards so as to the network can automatically focus on the hard negative samples and more fully learn the characteristic of them. With a multi-task learning training method in the training stage, the similarity measure and classification can be learned jointly. Experimental results on UCF101 and HMDB51 show that the proposed method can effectively improve the classification precision.
Dynamic Object Localization Based on Radio Frequency Identification and Laser Information
Ran LIU, Gaoli LIANG, Heng WANG, Yulu FU, Jing HE, Hua ZHANG
, doi: 10.11999/JEIT171088
[Abstract](5) [FullText HTML](6) [PDF 2439KB](4)
Recent researches show great interests in localizing dynamic objects through cost-effective technologies. Laser or visual-based approaches have to solve the singularity and occlusion problem from the environment. Radio Frequency IDentification (RFID) is used as a preferred technology to address these issues, due to the unique identification and the communication without line of sight. In this paper, an innovative method is proposed to localize precisely a dynamic object equipped with an RFID tag by fusing laser information RFID information. A particle filter is used to fuse RFID signal strength, phase information, and laser ranging data. Particularly, a pre-trained signal strength-based model is used to incorporate the signal strength information. Then, the laser ranging data is divided into different clusters and the velocities of these clusters are compared with the RFID phase velocity. Matching results of both velocities are used to confine the locations of the particles during the update stage of the particle filtering. The proposed approach is verified by several experiments on a SCITOS service robot and results show that the proposed approach provides better localization accuracy when compared with laser-based approach and the signal strength-based approach.
Verifiable Multi-keyword Search Encryption Scheme with Attribute Revocation
Jin SUN, Xiaojing WANG, Shangping WANG, Lili REN
, doi: 10.11999/JEIT180237
[Abstract](24) [FullText HTML](21) [PDF 1443KB](2)
In recent years, searchable encryption technology and fine-grained access control attribute encryption is widely used in cloud storage environment. Considering that the existing searchable attribute-based encryption schemes have some flaws: It only support single-keyword search without attribute revocation. The single-keyword search may result in the waste of computing and broadband resources due to the partial retrieval from search results. A verifiable multi-keyword search encryption scheme that supports revocation of attributes is proposed. The scheme allows users to detect the correctness of cloud server search results while supporting the revocation of user attributes in a fine-grained access control structure without updating the key or re-encrypting the ciphertext during revocation stage. The aforementioned scheme is proved by the deterministic linearity hypothesis, and the relevant analysis results indicate that it can resist the attacks of keyword selection and the privacy of keywords in the random oracle model with high computational efficiency and storage effectiveness.
Relative Mobility Prediction Based k-Hop Clustering Algorithm in AdHoc Networks
Luoming MENG, Yanfu JIANG, Yanjun LIU, Han SU, Siya XU, Feng QI
, doi: 10.11999/JEIT180192
[Abstract](7) [FullText HTML](7) [PDF 2430KB](1)
To solve the problem of network structure change and route failure caused by random movement of network nodes, a relative mobility prediction based k-hop clustering algorithm is proposed, the movement of nodes are analyzed and predicted, the cluster structure is adjusted adaptively, the stability of cluster structure is improved. First, the Doppler shift is used to calculate the relative moving speed and obtain the link expiration time between nodes. Then, during the cluster formation stage, the MAX-MIN heuristic algorithm is used to select the cluster head according to the average link expiration time of the node. Furthermore, during the cluster maintenance stage, a network adaptive adjustment method is proposed based on node motion. On the one hand, the node information transmission cycle is adjusted to balance the data overhead and accuracy; On the other hand, the cluster structure is adjusted by predicting the link disconnection to reduce link reconstruction time and improve the quality of network operation. Simulation results show that the proposed algorithm can effectively prolong the duration of cluster head and improve the stability of cluster structure in dynamic environment.
Wi-Fi Indoor Localization Based on Hybrid Hypothesis Test of Signal Distribution
Mu ZHOU, Xiaolong GENG, Liangbo XIE, Zengshan TIAN, Yacong WEI
, doi: 10.11999/JEIT180147
[Abstract](135) [FullText HTML](82) [PDF 1536KB](27)
Wi-Fi indoor localization technique is one of the current research hotspots in the field of mobile computing, however, the conventional location fingerprinting based localization scheme does not consider the diversity of Wi-Fi signal distribution in the complicated indoor environment, resulting in the low robustness of indoor localization system. To address this problem, a new hybrid hypothesis test of signal distribution for Wi-Fi indoor localization is proposed. Specifically, the Jarque-Bera (JB) test is conducted to examine the normality of Wi-Fi signal distribution at each Reference Point (RP). Then, according to the different Wi-Fi signal distributions, the hybrid Mann-Whitney U test and T test approaches are used to construct the set of matching reference points with the purpose of realizing the area localization. Finally, by calculating the K-Nearest Neighbor (KNN) of matching reference points in the located area, the location coordinate of the target is obtained. The experimental results indicate that the proposed approach is featured with higher localization accuracy as well as stronger system robustness compared with the conventional Wi-Fi indoor localization approaches.
Upper Bound Estimation of Average Differential Probability and Average Linear Chains Probability of Lai-Massey Structure
Ruya FAN, Chenhui JIN, Ting CUI
, doi: 10.11999/JEIT180196
[Abstract](421) [FullText HTML](302) [PDF 359KB](72)
Lai-Massey structure is a block cipher structure developed from IDEA algorithm. FOX is the representative of this cipher structure. In this paper, the keys are assumed to be generated independently and uniform randomly, and then the provable security against differential and linear cryptanalysis of Lai-Massey structure is studied from two aspects: the upper bound of the average differential probability and the upper bound of the average linear chains probability with the given starting and ending points. This paper proves that when \begin{document}$r{\rm{ = }}2$\end{document} , the average differential probability \begin{document}$ \le p{}_{\max }$\end{document} . With the F function of the Lai-Massey structure is orthomorphism, this paper proves that when \begin{document}$r \ge 3$\end{document} , the average differential probability \begin{document}$ \le p_{\max }^2$\end{document} . A similar conclusion is obtained for the linear chains with a given starting and ending point.
Forward Derivation and Analysis for 3-D Scattering Center Position of Radar Target
Lei ZHANG, Siyuan HE, Guoqiang ZHU, Yunhua ZHANG, Hongcheng YIN, Hua YAN
, doi: 10.11999/JEIT180115
[Abstract](2) [FullText HTML](2) [PDF 1463KB](1)
To link better scattering centers with target structures, a forward method is presented to deduce the component-level 3-D scattering center position of radar target under the mechanisms of single and double scattering based on target geometric model. Under the mechanism of double scattering, the principle and method for determining the ray equivalent position is introduced especially under the situation of strong scattering. As for other weak scattering situations, the equivalent transformation is used to transform the weak scattering situations to the strong one. Finally, this position derivation method is applied to the models of right dihedral angle, obtuse dihedral angle, SLICY and T72 tank to deduce and analyze their component-level scattering center positions. The corresponding simulated or actual SAR images are used for contrast to validate the accuracy of the position derivation method.
Orthogonal Opposition Based Firefly Algorithm
Lingyun ZHOU, Lixin DING, Maode MA, Wan TANG
, doi: 10.11999/JEIT180187
[Abstract](8) [FullText HTML](7) [PDF 809KB](1)
Firefly Algorithm (FA) may suffer from the defect of low convergence accuracy depending on the complexity of the optimization problem. To overcome the drawback, a novel learning strategy named Orthogonal Opposition Based Learning (OOBL) is proposed and integrated into FA. In OOBL, first, the opposite is calculated by the centroid opposition, making full use of the population search experience and avoiding depending on the system of coordinates. Second, the orthogonal opposite candidate solutions are constructed by orthogonal experiment design, combining the useful information from the individual and its opposite. The proposed algorithm is tested on the standard benchmark suite and compared with some recently introduced FA variants. The experimental results verify the effectiveness of OOBL and show the outstanding convergence accuracy of the proposed algorithm on most of the test functions.
Improved No-reference Noisy Image Quality Assessment Based on Masking Effect and Gradient Information
Hongyan LUO, Ziyan ZHU, Rui LIN, Zhen LIN, Yanjian LIAO
, doi: 10.11999/JEIT180195
[Abstract](7) [FullText HTML](8) [PDF 2303KB](1)
Heavy computational burden, or complex training procedure and poor universality caused by the manual setting of the fixed thresholds are the main issues associated with most of the noise image quality evaluation algorithms using domain transformation or machine learning. As an attempt for solution, an improved spatial noisy image quality evaluation algorithm based on the masking effect is presented. Firstly, according to the layer-layer progressive rule based on Hosaka principle, an image is divided into sub-blocks with different sizes that match the frequency distribution of its content, and a masking weight is assigned to each sub-block correspondingly. Then noise in the image is detected through the pixel gradient information extraction, via a two-step strategy. Following that, the preliminary evaluation value is obtained by using the masking weights to weighting the noise pollution index of all the sub-blocks. Finally, the correction and normalization are carried out to generate the whole image quality evaluation parameter——i.e. Modified No-Reference Peak Signal to Noise Ratio (MNRPSNR). Such an algorithm is tested on LIVE and TID2008 image quality assessment database, covering a variety of noise types. The results indicate that compared with the current mainstream evaluation algorithms, it has strong competitiveness, and also has the significant effects in improving the traditional algorithm. Moreover, the high degree of consistency to the human subjective feelings and the applicability to multiple noise types are well demonstrated.
Method for Frequency Error Compensation Based on Raw Data for SAR System with Bandwidth Synthesis
Sujuan FANG, Guangzuo LI, Yifei ZHANG, Wenxian YU, Yirong WU
, doi: 10.11999/JEIT180079
[Abstract](88) [FullText HTML](41) [PDF 1735KB](11)
To improve the resolution of the SAR system, radar bandwidth should be improved. By means of synthetic bandwidth, wide bandwidth can be achieved with less hardware complexity. For frequency band synthesis SAR system, frequency difference should be accurately known. However, in the real measurement situation, the frequency difference may drift and should be estimated based on the raw data. In this manuscript, an effective method is proposed to estimate the frequency difference error and compensate the phase error. Based on the relation between the interferometric phase of subband echoes and frequency difference, the frequency difference drift is estimated. The interferometry between subband images yields the interferometric image. It is observed that in the yielded image, phase varies with range and the slope is proportional to the frequency difference. Also, the phase is redundant along azimuth. Based on the redundancy along azimuth, a new vector is formed. The vector is a sinusoidal signal with the frequency value corresponding to the relative range shift. Frequency analysis yields the value of the frequency difference error. Based on the proposed method, the SAR image is improved. The effectiveness of the method is verified by processing the real SAR data.
Detection of Sound Event under Low SNR Using Multi-band Power Distribution
Ying LI, Lingfei WU
, doi: 10.11999/JEIT180180
[Abstract](71) [FullText HTML](33) [PDF 2784KB](3)
As to the problem of sound event detection in low Signal-Noise-Ratio (SNR) noise environments, a method is proposed based on discrete cosine transform coefficients extracted from multi-band power distribution image. First, by using gammatone spectrogram analysis, sound signal is transformed into multi-band power distribution image. Next, 8×8 size blocking and discrete cosine transform are applied to analyze the multi-band power distribution image. Based on the main Zigzag coefficients which are scanned from the discrete cosine transform coefficients, features of sound event are constructed. Finally, features are modeled and detected through random forests classifier. The results show that the proposed method achieves a better detection performance in low SNR comparing to other methods.
Adaptive Affine Propagation Clustering Algorithm for WiFi Indoor Positioning
Jiusong HU, Hongli LIU, Guoxuan XIAO, Kun XU
, doi: 10.11999/JEIT180186
[Abstract](2) [FullText HTML](2) [PDF 2704KB](0)
There are a large number of indoor WiFi signals which can be used for indoor positioning. Although many WiFi indoor positioning technology is proposed, it's positioning accuracy still does not meet the actual application requirements. For this problem, an Adaptive Affinity Propagation Clustering (AAPC) algorithm is proposed to improve the clustering quality of WiFi fingerprint, thus improving the positioning accuracy. The AAPC algorithm generates different clustering results by dynamically adjusting parameters, then cluster validity indices are used to select the best ones. A large number of real environmental data are collected and tested. The experimental results show that the clustering results generated by AAPC algorithm have higher positioning accuracy.
Network Slice Virtual Resource Allocation Algorithm Based on Constrained Markov Decision Process in Non-orthogonal Multiple Access
Lun TANG, Yingjie SHI, Xixi YANY, Qianbin CHEN
, doi: 10.11999/JEIT180131
[Abstract](6) [FullText HTML](9) [PDF 1438KB](1)
An adaptive virtual resource allocation algorithm is proposed based on Constrained Markov Decision Process (CMDP) for wireless access network slice virtual resource allocation. First of all, this algorithm in the Non-Orthogonal Multiple Access (NOMA) system, uses the user outage probability and the slice queues as constraints, uses the total rate of slices as a reward to build a resource adaptive problem using the CMDP theory. Secondly, the post-decision state is defined to avoid the expectation operation in the optimal value function. Furthermore, aiming at the problem of " dimensionality disaster” of MDP, based on the approximate dynamic programming theory, a basis function for the assignment behavior is designed to replace the post-decision state space and to reduce the computational dimension. Finally, an adaptive virtual resource allocation algorithm is designed to optimize the slicing performance. The simulation results show that the algorithm can improve the performance of the system and meet the service requirements of slicing.
Distributed Firewall Policy Based on Traffic Engineering in Software Defined Network
Jiugen SHI, Ji WANG, Jing ZHANG, Hao XU
, doi: 10.11999/JEIT180223
[Abstract](122) [FullText HTML](50) [PDF 1500KB](12)
Firewall policy is defined as access control rules in Software Definition Network (SDN), and distributing these ACL (Access Control List) rules across the networks, it can improve the quality of service. In order to reduce the number of rules placed in the network, the Heuristic Algorithm of Rules Allocation (HARA) of rule multiplexing and merging is proposed in this paper. Considering TCAM storage space of commodity switches and connected link traffic load of endpoint switches, a mixed integer linear programming model which minimize the number of rules placed in the network is established, and the algorithm solves the rules placement problem of multiple routing unicast sessions of different throughputs. Compared with the nonRM-CP algorithms, simulations show that HARA can save 18% TCAM at most and reduce the bandwidth utilization rate of 13.1% at average.
Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network
Xin GAO, Hui LI, Yi ZHANG, Menglong YAN, Zongshuo ZHANG, Xian SUN, Hao SUN, Hongfeng YU
, doi: 10.11999/JEIT180209
[Abstract](12) [FullText HTML](11) [PDF 2879KB](3)
Vehicle detection is one of the hotspots in the field of remote sensing image analysis. The intelligent extraction and identification of vehicles are of great significance to traffic management and urban construction. In remote sensing field, the existing methods of vehicle detection based on Convolution Neural Network (CNN) are complicated and most of these methods have poor performance for dense areas. To solve above problems, an end-to-end neural network model named DF-RCNN is presented to solve the detecting difficulty in dense areas. Firstly, the model unifies the resolution of the deep and shallow feature maps and combines them. After that, the deformable convolution and RoI pooling are used to study the geometrical deformation of the target by adding a small number of parameters and calculations. Experimental results show that the proposed model has good detection performance for vehicle targets in dense areas.
Power-frequency Electric Field Measurement Using a Micromachined Electric Field Sensor
Jie TONG, Yuqing LEI, Guohua LIU, He WANG, Xueming JIN, Pengfei YANG, Chunrong PENG
, doi: 10.11999/JEIT180217
[Abstract](7) [FullText HTML](8) [PDF 1623KB](0)
A novel power frequency electric field measurement system based on high-performance MEMS electric field sensing chips is developed. Based on cross-correlation detection principle, a power frequency electric field demodulation algorithm of MEMS sensing chips that can inhibit background interference noise is proposed. And a small-scale, high-resolution electric field measuring probe is designed. Moreover, the system overall structure scheme is designed for implementation of high-accuracy demodulation electric field signals. The test result under power lines shows that the plotted curves of the developed MEMS system are consistent with Narda EFA-300.
Low Complexity Iterative Parallel Interference Cancellation Detection Algorithms for Massive MIMO Systems
Bin SHEN, Shufeng ZHAO, Chun JIN
, doi: 10.11999/JEIT180111
[Abstract](134) [FullText HTML](68) [PDF 1584KB](12)
Based on interference cancellation method, a low complexity Iterative Parallel Interference Cancellation (IPIC) algorithm is proposed for the uplink of massive MIMO systems. The proposed algorithm avoids the high complexity matrix inversion required by the linear detection algorithm, and hence the complexity is maintained only at \begin{document}$({\cal O}({K^2}))$\end{document} . Meanwhile, the noise prediction mechanism is introduced and the noise-prediction aided iterative parallel interference cancellation algorithm is proposed to improve further the detection performance. Considering the residual inter-antenna interference, a low-complexity soft output signal detection algorithm is proposed as well. The simulation results show that the complexity of all the proposed signal detection methods are better than that of the MMSE detection algorithm. With only a small number of iterations, the proposed algorithm achieves its performance quite close to or even surpassing that of the MMSE algorithm.
Plane-wave Compounding with Short-lag Coherence Factor Weighting
Chichao ZHENG, Lunan ZHANG, Hao WANG, Hu PENG
, doi: 10.11999/JEIT180120
[Abstract](96) [FullText HTML](50) [PDF 5269KB](5)
The Coherent Plane-Wave Compounding (CPWC) algorithm is based on the recombination of several plane-waves with different steering angles, which can achieve high-quality images with high frame rate. However, CPWC ignores the coherence between the plane-wave imaging results. Coherence Factor (CF) weighted algorithm can effectively improve the imaging contrast and resolution, while it degrades the background speckle quality. A Short-Lag Coherence Factor (SLCF) algorithm for CPWC is proposed. SLCF uses the angular difference parameter to ascertain the order of the coherence factor and calculates the coherence factor for the plane-waves with small angular difference. Then, SLCF is utilized to weight CPWC to obtain the final images. Simulated and experimental results show that SLCF-weighted algorithm can improve the imaging quality in terms of lateral resolution and Contrast Ratio (CR), compared with CPWC. In addition, in comparison with CF and Generalized Coherence Factor (GCF) weighted algorithm, SLCF can achieve better background speckle quality and it has lower computational complexity.
Ambiguity Resolving and Imaging Algorithm for Multi-channel Forward-looking Synthetic Aperture Radar
Jingyue LU, Lei ZHANG, Guanyong WANG
, doi: 10.11999/JEIT180177
[Abstract](85) [FullText HTML](39) [PDF 1402KB](6)
The azimuth resolution of traditional synthetic aperture radar is only provided by synthetic aperture. However, in the forward looking area, the Doppler diversity is limited, so the imaging performance declines rapidly. And forward looking imaging also has the Doppler ambiguity problem. In this paper, an adaptive beam forming method with spatial confinement under ideal line track is proposed. The imaging quality of the positive forward region is improved effectively by combining the array of real aperture and synthetic aperture, and the Doppler solution is blurred by using the array space domain. First, the echo data is processed by High Squint SAR imaging to obtain the blurred image. Then the beam-forming is performed, weighted and coherent accumulated with each channel image, so as to resolve Doppler ambiguity and enhance the azimuth resolution. Simulation confirms the validity of the proposed approach.
Single Beacon Location Algorithm Based on Nonlinear Fading Filter Under Multiplicative Noise Background
Ziyao ZHU, Shuping HAN, Zhengdong GUO, Jianbo LIU
, doi: 10.11999/JEIT180239
[Abstract](109) [FullText HTML](49) [PDF 2109KB](10)
Single beacon location algorithm based on additive noise model can not accurately represent the actual characteristics of distance measurement, leading to a problem of model mismatch. A two step location algorithm considering the multiplicative noise characteristics is presented, which combines least squares algorithm and nonlinear fading filter. A range error model in the background of multiplicative noise is established baed on the analysis of the effective sound velocity error. The nonlinear fading filtering algorithm with single fading factor under multiplicative noise background is improved by introducing the attenuation factor which increases the track continuity. Using the least squares based pre-location process to solve the problem that the improved algorithm is sensitive to the initial value. The simulation and experimental data show that the location precision of the proposed algorithm is obviously better than the extended Kalman filtering algorithm under the additive noise background.
High Throughput Dual-mode Reconfigurable Floating-point FFT Processor
Xing WEI, Zhihong HUANG, Haigang YANG
, doi: 10.11999/JEIT180170
[Abstract](89) [FullText HTML](30) [PDF 2933KB](2)
In the advanced applications of real-time radar imaging and high-precision scientific computing systems, the design of high throughput and reconfigurable Floating-Point (FP) FFT accelerator is significant. Achieving high throughput FP FFT with low area and power cost poses a greater challenge due to high complexity of FP operations in comparison to fixed-point implementations. To address these issues, a serial of mixed-radix algorithms for 128/256/512/1024/2048-point FFT are proposed by decomposing long FFT into short implementations with cascaded radix-2k stages so that the complexity of multiplications can be significantly reduced. Besides, two novel fused FP add-subtract and dot-product units for dual-mode functionality are proposed, which can either compute on a pair of double precision operands or on two pairs of single precision operands in parallel. Thus, a high throughput dual-mode floating-point variable length FFT is designed. The proposed processor is implemented based on SMIC 28 nm CMOS technology. Simulation results show that the throughput and Signal-to-Quantization Noise Ratio (SQNR) in single-channel single precision and dual-channel half precision floating-point mode are 3.478 GSample/s, 135 dB and 6.957 GSample/s, 60 dB respectively. Compare to the other FP FFT, this processor can achieve 12 times improvement of normalized throughput-area ratio.
Research on SDN-based Load Balancing Technology of Server Cluster
Tianfang YU, Lanlan RUI, Xuesong QIU
, doi: 10.11999/JEIT180207
[Abstract](9) [FullText HTML](11) [PDF 1564KB](1)
Under the present network architecture, it is disadvantageous for scalability and service performance of server cluster to adopt hardware systems to realize load balancing of server cluster, because there are some restriction factors in such a method, including the difficulty of acquiring load nodes status and the complexity of redirecting traffic, etc. To solve the problem, a Load Balancing mechanism based on Software-Defined Networking (SDNLB) is proposed. With superiorities of SDN such as centralized control and flexible traffic scheduling, SDNLB monitors run states of servers and overall network load information by means of SNMP protocol and OpenFlow protocol in real time, and chooses the highest weight server as target server aiming for processing coming flows through the way of weight value calculation. On this basis, SDNLB takes full advantage of the optimal forwarding path algorithm to carry on traffic scheduling, and achieves the goal that raises utilization rate and processing performance of server cluster. An experiment platform is built to carry out simulation tests for overall performance of SDNLB, and the experiment results show that under the same network load conditions, SDNLB lowers effectively loads of server cluster, noticeably raises network throughput and bandwidth utilization, and reduces finish time and average latency of flows, compared with other load balancing algorithms.
Efficient Offline/Online Attribute Based Encryption with Verifiable Outsourced Decryption
Zhiyuan ZHAO, Lei SUN, Jiafu HU, Shie ZHOU
, doi: 10.11999/JEIT180122
[Abstract](128) [FullText HTML](50) [PDF 1106KB](6)
Attribute based encryption can provide data confidentiality protection and fine-grained access control for fog-cloud computing, however mobile devices in fog cloud computing system are difficult to bear the burdensome computing burden of attribute based encryption. In order to address this problem, an offline/online ciphertext-plicy attribute-based encryption scheme is presented with verifiable outsourced decryption based on the bilinear group of prime order. It can realize the offline/online key generation and data encryption. Simultaneously, it supports the verifiable outsourced decryption. Then, the formal security proofs of its selective chosen plaintext attack security and verifiability are provided. After that, the improved offline/online ciphertext-plicy attribute-based encryption scheme with verifiable outsourced decryption is presented, which reduces the number of bilinear pairings from linear to constant in the transformation phase. Finally, the efficiency of the proposed scheme is analyzed and verified through theoretical analysis and experimental simulation. The experimental results show that the proposed scheme is efficient and practical.
Multipath Clutter Rejection Approach Based on Carrier Domain Adaptive Iterative Filter in Passive Bistatic Radar
Zhixin ZHAO, Xinhua ZHOU, Sheng HONG, Tao WENG, Yuhao WANG
, doi: 10.11999/JEIT180097
[Abstract](223) [FullText HTML](43) [PDF 1635KB](6)
In passive bistatic radar systems, there exists the zero and non-zero Doppler shift multipath clutter in the surveillance channel. The multipath clutter affects the target detection. Temporal adaptive iterative filter such as Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Square (RLS) are often used to reject multipath clutter in passive bistatic radar, but these methods are only applicable to reject zero Doppler shift multipath clutter. To solve the problem of zero and non-zero Doppler shift multipath clutter, combined with the orthogonal frequency division multiplexing waveform features of digital broadcasting television signals, a clutter rejection algorithm is proposed based on carrier domain adaptive iterative filter. The algorithm utilizes the correlation of multipath clutter with the same Doppler shift at the same carrier frequency in subcarrier domain to reject the zero and non-zero Doppler shift multipath clutter. Simulation and experiment data processing results show the superiority of the proposed algorithm.
Magnetic Anomaly Detection Algorithm Based on Fractal Features in Geomagnetic Background
Luzhao CHEN, Wanhua ZHU, Peilin WU, Chunjiao FEI, Guangyou FANG
, doi: 10.11999/JEIT180307
[Abstract](46) [FullText HTML](40) [PDF 4297KB](14)
Magnetic Anomaly Detection (MAD) is a widely used passive target detection method. Its applications include surface warship target monitoring, underwater moving targets, and land target detection and identification. It is of great significance to research on the reliability detection method of weak magnetic anomaly signals based on geomagnetic background. This paper proposes a single sensor detection method based on the fractal characteristics of target magnetic anomaly signal based on the study of the differences in geomagnetic background and fractal characteristics of magnetic anomaly signals and conducts actual field test verification. The experimental results show that the method can accurately distinguish the geomagnetic background interference and magnetic anomaly signals, and can detect the weak magnetic anomaly signals in the geomagnetic background noise.
An Adaptive Directional MAC Protocol for Terahertz Wireless Personal Networks
Zhi REN, Yuhui LÜ, Zhaokun XU, Mingrui ZOU, Jieli TIAN
, doi: 10.11999/JEIT180306
[Abstract](19) [FullText HTML](14) [PDF 1157KB](2)
To reduce the beamforming training cost and network delay, make the best of Beacon and S-CAP sub-period in the existing Terahertz Wireless Personal Access Network (TWPAN) directional MAC protocols, an Adaptive Directional MAC (AD-MAC) protocol for TWPAN is proposed. AD-MAC adaptively uses the entire network cooperative beam training in a static scenario, and makes network nodes quickly respond to beam training frames based on historical information in a dynamic scenario. The reverse listening strategy is used to reduce the collision probability of same sector nodes. The control frame and data frame are transmitted simultaneously in the Beacon and S-CAP slot using time-slot reuse. Theoretical analysis verifies the effectiveness of AD-MAC. Also, simulation results show that, comparing with ENLBT-MAC, AD-MAC reduces about 21.84% of beamforming training cost and 22.70% of the average network delay in static scene, and reduces about 18.7% of beamforming training cost and 13.07% of the average network delay in dynamic scene.
Impossible Differential Cryptanalysis of the Digital Video Broadcasting-common Scrambling Algorithm
Xuan SHEN, Bing SUN, Guoqiang LIU, Chao LI
, doi: 10.11999/JEIT180245
[Abstract](140) [FullText HTML](107) [PDF 1052KB](41)
The Digital Video Broadcasting-Common Scrambling Algorithm (DVB-CSA) is a hybrid symmetric cipher. It is made up of the block cipher encryption and the stream cipher encryption. DVB-CSA is often used to protect MPEG-2 signal streams. This paper focuses on impossible differential cryptanalysis of the block cipher in DVB-CSA called CSA-BC. By exploiting the details of the S-box, a 22-round impossible differential is constructed, which is two rounds more than the previous best result. Furthermore, a 25-round impossible differential attack on CSA-BC is presented, which can recover 24 bit key. For the attack, the data complexity, the computational complexity and the memory complexity are 253.3 chosen plaintexts, 232.5 encryptions and 224 units, respectively. For impossible differential cryptanalysis of CSA-BC, the previous best result can attack 21-round CSA-BC and recover 16 bit key. In terms of the round number and the recovered key, the result significantly improves the previous best result.
Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter
Youlin GENG, Chengbo XIE, Chuan YIN, Lantu GUO, Xianyi WANG
, doi: 10.11999/JEIT180268
[Abstract](7) [FullText HTML](6) [PDF 1571KB](1)
The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to constructs the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.
A Precise Method for Calculating the Resolution of SAR Distribution Targets
Yanfei WANG, Xinxing CHEN
, doi: 10.11999/JEIT180294
[Abstract](9) [FullText HTML](7) [PDF 1323KB](1)
The separable probability is a significant criterion to evaluate the resolution characteristics of SAR distribution targets. On the basis of refining the separable condition of targets and taking the statistic characteristic of SAR distribution targets into consideration, a new separable judgment criterion for targets is proposed, and a precise calculation method of the separable probability is deduced. Besides, in order to simplify the calculation, the approximate calculation method with less computational complexity is presented. It is shown in the simulation results that the proposed method is in accordance with the actual situation, which can reflect the effect of the statistic characteristic of SAR distribution target on the resolution characteristic, and can provide theoretical support for the SAR image quality evaluation and system parameter design.
PolSAR Image Classification Based on Discriminative Clustering
Zhiqiang WEI, Haixia BI
, doi: 10.11999/JEIT180229
[Abstract](10) [FullText HTML](7) [PDF 3267KB](1)
This paper presents a novel unsupervised image classification method for Polarimetric Synthetic Aperture Radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, an energy function is designed for unsupervised PolSAR image classification by combining a supervised Softmax Regression (SR) model with a Markov Random Field (MRF) smoothness constraint. In this model, both the pixelwise class labels and classifiers are taken as unknown variables to be optimized. Starting from the initialized class labels generated by Cloude-Pottier decomposition and K-Wishart distribution hypothesis, the classifiers and class labels are iteratively optimized by alternately minimizing the energy function with respect to them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect. This approach is applied to real PolSAR benchmark data. Extensive experiments justify that the proposed approach can effectively classify the PolSAR image in an unsupervised way and produce higher accuracies than the compared state-of-the-art methods.
Gridless Sparse Method for Direction of Arrival Estimation for Two-dimensional Array
Jianshu WANG, Yangyu FAN, Rui DU, Guoyun LÜ
, doi: 10.11999/JEIT180340
[Abstract](2) [FullText HTML](2) [PDF 2144KB](0)
For the fact that current gridless Direction Of Arrival (DOA) estimation methods with two-dimensional array suffer from unsatisfactory performance, a novel girdless DOA estimation method is proposed in this paper. For two-dimensional array, the atomic L0-norm is proved to be the solution of a Semi-Definite Programming (SDP) problem, whose cost function is the rank of a Hermitian matrix, which is constructed by finite order of Bessel functions of the first kind. According to low rank matrix recovery theorems, the cost function of the SDP problem is replaced by the log-det function, and the SDP problem is solved by Majorization-Minimization (MM) method. At last, the gridless DOA estimation is achieved by Vandermonde decomposition method of semidefinite Toeplitz matrix built by the solutions of above SDP problem. Sample covariance matrix is used to form the initial optimization problem in MM method, which can reduce the iterations. Simulation results show that, compared with on-grid MUSIC and other gridless methods, the proposed method has better Root-Mean-Square Error (RMSE) performance and identifiability to adjacent sources; When snapshots are enough and Signal-Noise-Ratio (SNR) is high, proper choice of the order of Bessel functions of the first kind can achieve approximate RMSE performance as that of higher order ones, and can reduce the running time.
Stackelberg Game-based Resource Allocation Strategy in Virtualized Wireless Sensor Network
Ruyan WANG, Hongjuan LI, Dapeng WU
, doi: 10.11999/JEIT180277
[Abstract](124) [FullText HTML](67) [PDF 1478KB](18)
Virtualization is a new technology that can effectively solve the low resource utilization and service inflexibility problem in the current Wireless Sensor Network (WSN). For the resource competition problem in virtualized WSN, a multi-task resource allocation strategy based on Stackelberg game is proposed. According to the different Quality of Service (QoS) requirements of the business carried by Virtual Sensor Network Request (VSNR), the importance of multiple VSNRs is quantified. Then, the optimal price of WSN and the optimal resource requirements of VSNRs are obtained by using distributed iteration method. Finally, the resource corresponding to multiple VSNRs is acquired according to optimal price and optimal resource allocation determined by Nash equilibrium. The simulation results show that the proposed strategy can not only meet the diversified needs of users, but also improve the resource utilization of nodes and links.
Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Detection
Baozhu LI, Jian GUAN, Yunlong DONG
, doi: 10.11999/JEIT180303
[Abstract](96) [FullText HTML](50) [PDF 2285KB](11)
To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors detection is proposed according to the statistical characteristics of Gaussian random vectors. The state estimation decomposition equation is firstly derived in the Modified Polar Coordinates (MPC). The track vectors are obtained by the real state cancellation method. Second, In order to eliminate most non-homologous target tracks, the rough association is performed according to the features of the azimuthal rate and Inverse-Time-to-Go (ITG). Finally, the track-to-track association of radar and ESM is extracted based on track vectors chi-square distribution. The effectiveness of the proposed algorithm are verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
Application of Residual Network to Infant Crying Recognition
Xiang XIE, Liqiang ZHANG, Jing WANG
, doi: 10.11999/JEIT180276
[Abstract](23) [FullText HTML](18) [PDF 1311KB](4)
The deep learning model based on the residual network and the spectrogram are used to recognize infant crying in this paper. The corpus has balanced proportion of infant crying and non-crying samples. Finally, through the 5-fold cross validation, compared with three models of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and the cochleagram residual network based on Gammatone filters (GT-Resnet), the spectrogram based residual network gets the best F1-score of 0.9965 and satisfies requirements of real time. This paper proves that the spectrogram could react acoustics features intuitively and comprehensively in the recognition of infant crying. The residual network based on spectrogram is a good solution to infant crying recognition problem.
Co-saliency Detection Based on Convolutional Neural Network and Global Optimization
Zemin WU, Jun WANG, Lei HU, Chang TIAN, Mingyong ZENG, Lin DU
, doi: 10.11999/JEIT180241
[Abstract](9) [FullText HTML](7) [PDF 3529KB](1)
To solve the problems in current co-saliency detection algorithms, a novel co-saliency detection algorithm is proposed which applies fully convolution neural network and global optimization model. First, a fully convolution saliency detection network is built based on VGG16Net. The network can simulate the human visual attention mechanism and extract the saliency region in an image from the semantic level. Second, based on the traditional saliency optimization model, the global co-saliency optimization model is constructed, which realizes the transmission and sharing of the current superpixel saliency value in inter-images and intra-image through superpixel matching, making the final saliency map has better co-saliency value. Third, the inter-image saliency value propagation constraint parameter is innovatively introduced to overcome the disadvantages of superpixel mismatching. Experimental results on public test datasets show that the proposed algorithm is superior over current state-of-the-art methods in terms of detection accuracy and detection efficiency, and has strong robustness.
Research on Placement Algorithm of Service Function Chaining Oriented to Software Defined Networking
Yu LU, Yicen LIU, Xi LI, Xingkai CHEN, Wenxin QIAO, Liyun CHEN
, doi: 10.11999/JEIT180264
[Abstract](9) [FullText HTML](9) [PDF 1575KB](4)
For Network Function Virtualization (NFV) environment, the existing placement methods can not guarantee the mapping cost while optimizing the network delay, a service function chaining optimal placement algorithm is proposed based on the IQGA-Viterbi learning algorithm. In the training process of Hidden Markov Model (HMM) parameters, the traditional Baum-Welch algorithm is easy to fall into the local optimum, so the quantum genetic algorithm is proposed which can better optimize the model parameters. In each iteration, the improved algorithm maintains the diversity of feasible solutions and expands the scope of the spatial search by replicating the best fitness population with equal proportion, thus improving the accuracy of the model parameters. In the process of solving Hidden Markov chain, to overcome the problem that can not be directly observed for hidden sequences, Viterbi algorithm can solve the implicit sequences exactly and solve the problem of optimal service paths in the directed graph. Experimental results show that the network delay and mapping costs are lower compared with the existing algorithms. In addition, the acceptance ratio of requests is raised.
Fast Cross-range Scaling for ISAR Imaging Based on Pseudo Polar Fourier Transform
Dong LI, Chengxiang ZHANG, Di ZHAO, Xiaoheng TAN, Xichuan ZHOU, Muyang ZHAN
, doi: 10.11999/JEIT180299
[Abstract](5) [FullText HTML](6) [PDF 2752KB](0)
For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.
Remote Sensing Image Fusion Based on Optimized Dictionary Learning
Fan LIU, Xiaopeng PEI, Jing ZHANG, Zehua CHEN
, doi: 10.11999/JEIT180263
[Abstract](8) [FullText HTML](7) [PDF 5304KB](1)
In order to improve the fusion quality of panchromatic image and multi-spectral image, a remote sensing image fusion method based on optimized dictionary learning is proposed. Firstly, K-means cluster is applied to image blocks in the image database, and then image blocks with high similarity are removed partly in order to improve the training efficiency. While obtaining a universal dictionary, the similar dictionary atoms and less used dictionary atoms are marked for further research. Secondly, similar dictionary atoms and less used dictionary atoms are replaced by panchromatic image blocks with the largest difference from the original sparse model to obtain an adaptive dictionary. Furthermore the adaptive dictionary is used to sparse represent the intensity component and panchromatic image, the modulus maxima coefficients in the sparse coefficients of each image blocks are separated to obtain maximal sparse coefficients, and the remaining sparse coefficients are called residual sparse coefficients. Then, each part is fused by different fusion rules to preserve more spectral and spatial detail information. Finally, inverse IHS transform is employed to obtain the fused image. Experiments demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than its counterparts.
An Electromagnetic Environment Complex Evaluation Algorithm Based on Fast S-transform and Time-frequency Space Model
Baiqiang YIN, Shudong WANG, Yigang HE, Lei ZUO, Bing LI, Zhen CHENG
, doi: 10.11999/JEIT180256
[Abstract](7) [FullText HTML](9) [PDF 1395KB](1)
For qualitative and quantitative complex evaluation problem of electromagnetic environment. This paper proposes a novel electromagnetic environment complex evaluation algorithm based on fast S-transform and time-frequency space model, which can count time-complex, frequency-complex and energy-complex simultaneously. At the same time, the computation methods and concept of qualitative and quantitative evaluation degree are introduced in this paper. To overcome the limitations of the traditional, F-norm and root-mean-square are selected as two important evaluation indicators, which have the advantage in accurate evaluation. Simulation results show that the proposed method is accurate and effective to reflect the intensity degree of electromagnetic interference; Meanwhile, the interference experiment of bus card confirms the correctness of the time-frequency space model. The experimental test results verify the correctness of the evaluators mentioned in this paper.
Deployment Algorithm of Service Function Chain of Access Network Based on Stochastic Learning
Qianbin CHEN, Youchao YANG, Yu ZHOU, Guofan ZHAO, Lun TANG
, doi: 10.11999/JEIT180310
[Abstract](20) [FullText HTML](14) [PDF 1672KB](3)
To solve problem of the high delay caused by the change of physical network topology under the 5G access network C-RAN architecture, this paper proposes a scheme about dynamic deployment of Service Function Chain (SFC) in access network based on Partial Observation Markov Decision Process (POMDP). In this scheme, the system observes changes of the underlying physical network topology through the heartbeat packet observation mechanism. Due to the observation errors, it is impossible to obtain all the real topological conditions. Therefore, by the partial awareness and stochastic learning of POMDP, the system dynamically adjust the deployment of the SFC in the slice of the access network when topology changes, so as to optimize the delay. Finally, point-based hybrid heuristic value iteration algorithm is used to find SFC deployment strategy. The simulation results show that this model can support to optimize the deployment of SFC in the access network side and improve the access network’s throughput and resource utilization.
Design and Implementation for Chroma Extensions Video Coding Based on AVS2 Platform
Shuhui WANG
, doi: 10.11999/JEIT180154
[Abstract](4) [FullText HTML](3) [PDF 1976KB](0)
Chroma extensions video coding is a hot topic in the field of video coding. Chroma extensions video coding scheme based on AVS2 platform is proposed. The most direct solution is pseudo444/422 coding. In this method, chroma component in the input image is down sampled by averaging adjacent samples. The core coding modules are still 420 coding. Further, this paper seamlessly extends intra prediction and loop filter to the 444/422 chroma format to implement 444/422 intra prediction coding. The experimental results show that compared with pseudo444/422 coding, in the case of high bit rate, the average U/V BD-rate saving is 31.44%/31.72% and 18.85%/19.30% for 444 and 422 test sequences respectively, with negligible increase of Y BD-rate (0.5% on average). The modification of the 422 chroma intra prediction algorithm achieves up to 5.66% Y/U/V BD-rate reduction. 444/422 intra prediction coding provides similar or better coding performance than HEVC RExt coding at low bitrates.
Estimation of Ionospheric Incoherent Scatter Spectrum and Autocorrelation Function
Lin LI, Chengjiao HAN, Zonghua DING, Hongbing JI, Yajie WANG
, doi: 10.11999/JEIT180331
[Abstract](3) [FullText HTML](3) [PDF 1276KB](0)
Incoherent scatter spectrum plays an important role in studying the physical parameters of the ionosphere. The conventional theoretical model of incoherent scatter spectrum for derivation and calculation is extremely complicated and the model of the autocorrelation function can not be obtained . In this paper, the simplified model of ionospheric incoherent scatter spectrum is re-derived and the corresponding autocorrelation function is proposed. In the procedure of traditional incoherent scattering radar signal processing, the autocorrelation function is imbalance at different delays. This is mainly because the range resolution of zero-lag is very low, which affects the estimated performance of ionospheric scatter spectrum. Focus on this problem, a method based on data fitting is proposed to estimate the autocorrelation at zero-lag. Considering the computational complexity, a fast implementation method by polynomial functions is proposed to approach the autocorrelation function. Finally, experimental results on real echo data demonstrate the correctness and efficiency of the proposed method, which is of great significance for ionospheric detection.
Gesture Recognition with Multi-dimensional Parameter using a FMCW Radar
Yong WANG, Jinjun WU, Zengshan TIAN, Mu ZHOU, Shasha WANG
, doi: 10.11999/JEIT180485
[Abstract](3) [FullText HTML](3) [PDF 1738KB](0)
A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance, Doppler and angle parameters of the gesture target. To realize feature extraction and classification accurately, an end-to-end structured Range-Doppler-Angle of Time (RDA-T) multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion. The experimental results reveal that using the combined gestures information of distance, Doppler and angle for multi-parameter learning, the proposed scheme resolves the problem of low information quantity of single-dimensional gesture recognition methods, and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%~8%.
Research of Chronic Obstructive Pulmonary Disease Monitoring System Based on Four-line Turbine-type
Rongjian ZHAO, Wang ZHOU, Mingfang TANG, Xianxiang CHEN, Lidong DU, Zhan ZHAO, Ting YANF, Qingyuan ZHAN, Zhen FANG
, doi: 10.11999/JEIT180315
[Abstract](3) [FullText HTML](3) [PDF 2817KB](0)
To improve accuracy and reliability of the traditional turbine-vital capacity meter, a novel four-line turbine-detection method is presented for the high precision and high reliability Chronic Obstructive Pulmonary Disease (COPD) monitoring system. On the hardware, a four-line breath signal acquisition circuit is designed following the four-line turbine-type detection method, which improves the resolution of the optical path through reasonable components arrangement. On the software, a linear regression algorithm is used to obtain early screening and diagnostic indicators such as Forced Vital Capacity (FVC), Peak Expiratory Flow (PEF) and so on. The standard Fluke air flow analyzer is used for data calibration, compared with the traditional medical turbine-type lung function meter: FVC average relative error is reduced from 1.98% to 1.47% and PEF average relative error is reduced from 2.04% to 1.02%. It is showed that the expiratory parameters of the four-line turbine-type COPD monitoring system is more accurate and reliable than that of the traditional COPD system which is suitable for early screening and accurate diagnosis of COPD. Combined with pulse oxygen saturation, End-tidal CO2, it can be used to achieve the medical care for COPD and play an important role to early detect and control of disease for moderate or severe COPD patients.
A Diverse Virtual Optical Network Mapping Strategy Based on Security Awareness in Elastic Optical Networks
Huanlin LIU, Zhenyu LIN, Xin WANG, Yong CHEN, Min XIANG, Yue MA
, doi: 10.11999/JEIT180335
[Abstract](2) [FullText HTML](2) [PDF 2367KB](0)
Due to the probabilistic failure of the optical fiber of the underlying network in the virtual environment, traditional full protection configures one protection path at least which leads to high resource redundancy and low acceptance rate of the virtual network. In this paper, a Security Awareness-based Diverse Virtual Network Mapping (SA-DVNM) strategy is proposed to provide security guarantee in the event of failures. In SA-DVNM, the physical node weight formula is designed by considering the hops between nodes and the bandwidth of adjacent links, besides, a path-balanced link mapping mechanism is proposed to minimize the overloaded link. For improving the acceptability of virtual network, SA-DVNM strategy designs a resource allocation mechanism that allows path cut when a single path is unavailable for low security. Considering the difference of time delay to ensure the security of delay-sensitive services, a multipath routing spectrum allocation method based on delay difference is designed to optimize the routing and spectrum allocation for SA-DVNM strategy. The simulation results show that the proposed SA-DVNM strategy can improve the spectrum utilization and virtual optical network acceptance rate in the probabilistic fault environment, and reduce the bandwidth blocking probability.
New Privacy Preserving Aggregate Signcryption for Heterogeneous Systems
Yulei ZHANG, Xiangzhen LIU, Xiaoli LANG, Wenjuan CHEN, Caifen WANG
, doi: 10.11999/JEIT180249
[Abstract](10) [FullText HTML](12) [PDF 397KB](0)
The privacy preserving aggregate signcryption for heterogeneous systems can ensure the confidentiality and unforgeability of the data between heterogeneous cryptosystems, it also can provide multi-ciphertext batch verification. This paper analyzes the security of a scheme with privacy-preserving aggregate signcryption heterogeneous, and points out that the scheme can not resist the attack of malicious Key Generating Center (KGC), it can forge a valid ciphertext. In order to improve the security of the original scheme, a new heterogeneous aggregation signature scheme with privacy protection function is proposed.The new scheme overcomes the security problems existing in the original scheme and ensures the data transmission between the certificateless public key cryptography and the identity-based public key cryptographic, and the security of the new scheme is proved under the random oracle model. Efficiency analysis shows that the new program is equivalent to the original one.

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Journal of electronics and information 2018-10 catalogue
2018, 40(10)   
[Abstract](62) [PDF 371KB](23)
Research on Resource Allocation Algorithm for D2D Communications Underlaying LTE-A Networks
Zhihong QIAN, Shuangye YAN, Chunsheng TIAN, Xin WANG
2018, 40(10): 2287 -2293   doi: 10.11999/JEIT180043
[Abstract](142) [FullText HTML](83) [PDF 1313KB](17)
Uplink resource allocation problem in Device-to-Device (D2D) communications underlaying LTE-A networks is analyzed. First, the problem is modeled as a Mixed Integer NonLinear Programming (MINLP). Then the algorithm calculates each waiting user’s identity list in accordance with the preference for channels to form coalitions. On the premise of guaranteeing the Quality of Service (QoS) of users in the system, the suitable resource and reuse partner are assigned to each user through Maximum Weighted Bipartite Matching (MWBM). The simulation results show that this algorithm can break the constraint that D2D pairs can only stay on dedicated or reused mode when they are on data transmission, and expand the range of available resource for D2D users, which increases effectively the system sum-rate compared with the existing algorithm.
Achievable Rate Analysis on Massive MIMO-OFDM Systems with Low-resolution ADC
Cheng TAO, Wenbo ZHENG, Yongzhi LI, Liu LIU
2018, 40(10): 2294 -2300   doi: 10.11999/JEIT180088
[Abstract](21) [FullText HTML](18) [PDF 1249KB](7)
The achievable rate performance for the massive MIMO-OFDM system is investigated, where each antenna is equipped with low-resolution Analog-to-Digital Converters (ADC) and the Maximum Ratio Combining (MRC) receiver is assumed to be employed. The closed-form expression for the uplink achievable rate is firstly derived by using the Additive Quantization Noise Model (AQNM) model, which reforms the nonlinear quantization function into a linear one. Then the performance between the low-resolution quantization system and the conventional system with infinite resolution ADCs is compared based on the derived closed-form expression. Simulation results are presented to verify the analytical results. In addition, it is illustrated that the performance loss of using low-resolution ADCs can be compensated for by deploying more antennas at the base station.
A Non-stationary 3D Spatial Channel Model Based on Stochastic Scattering Cluster
Wei ZHANG, Jingjing DUAN, Yansong WANG
2018, 40(10): 2301 -2308   doi: 10.11999/JEIT170929
[Abstract](173) [FullText HTML](51) [PDF 1993KB](12)
To describe the near field effect and the non-stationary characteristic of the Massive MIMO channel, a non-stationary 3D spatial channel model based on stochastic scattering clusters for Massive MIMO systems is proposed. The parabolic wave instead of the spherical wave is used to model the near field effect, and the channel capacity of the model is analyzed under parabolic wavefront condition. For non-stationary properties of massive MIMO channel, the effective scattering clusters set of transmitting and receiving antenna elements is determined based on the effective probability of scattering clusters, and the stochastic evolution of scattering clusters along the antenna array axis is modeled to describe properly the appearance and disappearance of scattering clusters. Simulation results demonstrate that parabolic wavefront and the stochastic evolution of effective scattering clusters are good candidates to model Massive MIMO channel characteristics.
Message Passing Multiuser Detection Algorithm for SCMA Based on Partial Codewords Searching
Wenping GE, Xuewan ZHANG, Xiong WU, Wenli DAI
2018, 40(10): 2309 -2315   doi: 10.11999/JEIT171073
[Abstract](78) [FullText HTML](36) [PDF 2017KB](7)
Sparse Code Multiple Access (SCMA), based on Message Passing Algorithm (MPA) for multiuser detection, is a Non-Orthogonal Multiple Access (NOMA) scheme proposed to meet the demands of the future 5G communication. For the problem that the MPA has the characteristics of high algorithm complexity, some statistical results for the Probability Density Function (PDF) of received signal at various Signal to Noise Ratio (SNR) are first derived. Then, data mapping relationship between resources node and users node is fully considered based on the non-orthogonal property of SCMA, therefore a Partial Codewords Searching of MPA (PCS-MPA) is proposed with threshold decision scheme of PDF. Simulations results show that the proposed PCS-MPA can reduce the complexity without changing the Bit Error Ratio (BER), especially at the case of high SNR.
Single Base Station Localization Algorithm Based on B-LM Ring of Scattering Model Using NLOS Information
Zengshan TIAN, Yueyue SHU, Mu ZHOU, Yong LI, Ze LI
2018, 40(10): 2316 -2322   doi: 10.11999/JEIT171222
[Abstract](152) [FullText HTML](38) [PDF 2000KB](7)
Considering the requirements of time and data synchronization in multi-BS (Base Station) positioning in the current outdoor cellular network and the problem of signals’ detectability in area without service BS due to NLOS (Non-Line-Of-Sight) environment, a single base station localization algorithm based on B-LM (Broyden Fletcher Goldfarb Shanno-Levenberg Marquard) ring of scattering model using NLOS information is proposed. Firstly, the localization objective equation is constructed according to the geometric positions of the scatterers, the target, the base station and the NLOS multipath information. Then, the localization equation is transformed into the least square optimization problem. Finally, the B-LM algorithm based on Hessian matrix modification methodology in LM algorithm and the construction of second order partial derivative in quasi-Newton algorithm is proposed, which ensures the localization algorithm converges to the optimal solutions to obtain the target’s location. The simulation results show that the proposed single base station localization algorithm can achieve a high positioning accuracy in the NLOS environment for macrocell.
Artificial Noise Aided Secure Transmission in Dense Heterogeneous Cellular Networks with Imperfect Channel State Information
Yunjia XU, Kaizhi HUANG, Jun LI, Yajun CHEN
2018, 40(10): 2323 -2330   doi: 10.11999/JEIT180003
[Abstract](238) [FullText HTML](108) [PDF 1314KB](15)
To ensure secure transmission in dense heterogeneous cellular networks with imperfect Channel State Information (CSI), the influence of Artificial Noise (AN) on secure and reliable communication is analyzed, and a power split factor optimization model is presented to obtain the optimal value under different channel estimation accuracy. First, the connection outage probability and secrecy outage probability are deduced by considering the influence of channel estimation error on signal transmission and AN leakage. Then, a power split factor optimization model is presented, which maximizes the secrecy throughput subject to the security and reliability requirements. A K-dimensional search method is employed to solve the optimal power split factor of each tier. Finally, the numerical results verify that the AN transmission scheme with optimal power split factor can increase secrecy throughput by about 15%.
Non-orthogonal-code Index Modulation
Lijia GE, Zhilin JIANG, Sheng FENG, Qin YANG
2018, 40(10): 2331 -2336   doi: 10.11999/JEIT180023
[Abstract](103) [FullText HTML](43) [PDF 2020KB](9)
To deal with the problem that the bit error rate reduces obviously when the Code Index Modulation (CIM) is used to improve spectrum utilization, a novel Non-orthogonal-Code Index Modulation (N-CIM) is proposed. The bit stream of the transmitter is divided into mapping block of Pseudo Noise (PN) code and informational block of modulation, which are mapped into the index of the PN code and modulation symbol respectively. The real part and imaginary part of the modulation symbol are spread by selecting the identical activate PN code. Simulation and analysis results show that N-CIM outperforms CIM by about 2~3 dB in additive white Gaussian noise channel when the bit error rate is 10–5, and N-CIM outperforms CIM by about 2 dB in Rayleigh fading channel when the bit error rate is 10–2 at the same spectral efficiency.
Secondary User Power Control Aided Cooperative Spectrum Sensing
Bin SHEN, Zhiqiang WANG, Han QING
2018, 40(10): 2337 -2344   doi: 10.11999/JEIT171232
[Abstract](258) [FullText HTML](197) [PDF 1413KB](58)
In conventional cooperative spectrum sensing, the signal model is usually simplified as a single-stage channel environment where the Secondary Users (SUs) collect their spectrum data and report to the Fusion Center (FC) with the same transmit power. This hampers the FC from efficiently exploiting the space diversity gain beneath the data of different users. In order to solve this problem and control the user transmit power in reporting their data, three Optimal Power Control (OPC) schemes are proposed. When the Channel Statistic (CS) of the sensing channel and the reporting channel are perfectly known at the FC, a CS Aided Optimal Power Control (CSA-OPC) scheme is derived in closed-form, whereas when the CS is practically unavailable, Principal EigenVector aided OPC (PEV-OPC) and Blindly Weighted Multiple-EigenVector aided OPC (BWMEV-OPC) schemes are developed. Theoretical analysis and computer simulation verify that the propose OPC schemes greatly ameliorate the spectrum sensing performance, compared to the non-OPC aided cooperative spectrum sensing schemes.
Resources Balancing Algorithm Based on the Time-frequency Fragment Awareness for Virtual Optical Network Mapping
Huanlin LIU, Hao HU, Cuilian XIONG, Yong CHEN, Min XIANG, Yue MA
2018, 40(10): 2345 -2351   doi: 10.11999/JEIT171208
[Abstract](219) [FullText HTML](66) [PDF 1846KB](8)
In order to address the problems of the high bandwidth blocking probability and imbalance resources consumption in physical network during virtual optical network mapping, Fragmentation-Aware based on time and spectrum domain of Virtual Network Mapping (FA-VNM) algorithm is proposed. In the FA-VNM algorithm, the fragments problem in the time domain and the spectrum domain is considered. Fragment formula jointly considering the time fragment and spectrum fragment is devised to minimize the spectrum fragments. Further, in order to balance the network resources consumption, based on the FA-VNM, Load Balancing based on degree of Virtual Network Mapping (LB-VNM) algorithm is proposed. In the stage of node mapping, physical node average resource carrying capacity is introduced and the physical node with larger average resources carrying capacity is mapped first. In order to balance the resource consumption in physical path, weight value of physical path is calculated in the stage of link mapping. Then, according to the weight value of each physical path, virtual links are mapped to achieve the purpose of load balancing for reduce the blocking rate. Simulation results show that the algorithms can effectively reduce the blocking rate and improve the resources utilization.
Research on the Construction of Plateaued Functions
Tianfeng SUN, Bin HU, Yang YANG
2018, 40(10): 2352 -2357   doi: 10.11999/JEIT170965
[Abstract](112) [FullText HTML](39) [PDF 725KB](12)
Plateaued functions play a significant role in cryptography, coding theory and so on. In this paper, a new primary construction of plateaued function is given. Some cryptographic properties of the constructed plateaued functions are studied. It is shown that the existing primary constructions of plateaued function can be reduced to the proposed construction.
Novel Method for Outlier Nodes Detection and Localization in Wireless Sensor Networks
Junzheng JIANG, Jie YANG, Shan OUYANG
2018, 40(10): 2358 -2364   doi: 10.11999/JEIT171207
[Abstract](224) [FullText HTML](133) [PDF 1278KB](47)
The outlier nodes detection and localization in Wireless Sensor Networks (WSNs) is a crucial step in ensuring the accuracy and reliability of network data acquisition. Based on the theory of graph signal processing, a novel algorithm is presented for outlier detection and localization in WSNs. The new algorithm first builds the graph signal model of the network, then detect the location of the outlier based on the method of vertex-domain and graph frequency-domain joint analysis. Specifically speaking, the first step of algorithm is extracting the high-frequency component of the signal using a high-pass graph filter. In the second step, the network is decomposed into a set of sub-graphs, and then the specific frequency components of the output signal in sub-graphs are filtered out. The third step is to locate the suspected outlier center-nodes of sub-graphs based on the threshold of the filtered sub-graphs signal. Finally, the outlier nodes in the network are detected and located by comparing the set of nodes of each sub-graph with the set of suspected outlier nodes. Experimental results show that compared with the existing outlier detection methods in networks, the proposed method not only has higher probability of outlier detection, but also has a higher positioning rate of outlier nodes.
Time Series Method Clustering in User Behavior Based on Symmetric Kullback-Leibler Distance
Wenjing LI, Xiangjian ZENG, Meng LI, Peng YU
2018, 40(10): 2365 -2372   doi: 10.11999/JEIT180016
[Abstract](117) [FullText HTML](77) [PDF 2185KB](23)
Behavioral analysis of Internet users over time is a hot spot in user behavior analysis in recent years, usually clustering users is a way to find the feature of user behavior. Problems like poor computing performance or inaccurate distance metric exist in present research about clustering user time series data, which is unable to deal with large scale data. To solve this problem, a method for clustering time series in user behavior is proposed based on symmetric Kullback-Leibler (KL) distance. First time series data is transformed into probability models, and then a distance metric named KL distance is introduce, using partition clustering method, the different time distribution between different users. For the Large-scale feature of physical network data, each process of clustering is optimized based on the characteristics of KL distance. It also proves an efficient solution for finding the clustering centroids. The experimental results show that this method can improve the accuracy of 4% compared with clustering algorithm using the Euclidean distance metric or DTW metric, and the calculation time of this method is less a quantity degree than clustering algorithm using medoids centroids. This method is used to deal with user traffic data obtained in physical network which proves its application value.
Multi-model Real-time Compressive Tracking
Jianming ZHANG, Xiaokang JIN, Honglin WU, You WU
2018, 40(10): 2373 -2380   doi: 10.11999/JEIT171128
[Abstract](114) [FullText HTML](58) [PDF 3816KB](11)
Object tracking is easily influenced by illumination, occlusion, scale, background clutter, and fast motion, and it requires higher real-time performance. The object tracking algorithm based on compressive sensing has a better real-time performance but performs weakly in tracking when object appearance is changed greatly. Based on the framework of compressive sensing, a Multi-Model real-time Compressive Tracking (MMCT) algorithm is proposed, which adopts the compressive sensing to decrease the high dimensional features for the tracking process and to satisfy the real-time performance. The MMCT algorithm selects the most suitable classifier by judging the maximum classification score difference of classifiers in the previous two frames, and enhances the accuracy of location. The MMCT algorithm also presents a new model update strategy, which employs the fixed or dynamic learning rates according to the differences of decision classifiers and improves the precision of classification. The multi-model introduced by MMCT does not increase the computational burden and shows an excellent real-time performance. The experimental results indicate that the MMCT algorithm can well adapt to illumination, occlusion, background clutter and plane-rotation.
Shift-variant Similarity Learning for Person Re-identification
Bing CHEN, Yufei ZHA, Yunqiang LI, Shengjie ZHANG, Yuanqiang ZHANG
2018, 40(10): 2381 -2387   doi: 10.11999/JEIT180184
[Abstract](82) [FullText HTML](38) [PDF 1618KB](5)
The accuracy of pedestrian re-recognition mainly depends on the similarity measure and the feature learning model. The existing measurement methods have the characteristics of translation invariance, which make the training of network parameters difficult. Several existing feature learning models only emphasize the absolute distance between sample pairs, but ignore the relative distance between positive sample pairs and negative sample pairs, resulting in a weak discriminant feature in network learning. In view of the shortcomings of existing measurement methods, a distance measurement method of translation change is presented, which can effectively measure the similarity between images. To overcome the shortcomings of the feature learning model, based on the proposed translation distance metric, a new logistic regression model with enlarged intervals is proposed. By increasing the relative distance between the positive and negative sample pairs, the network can get more discriminant features. In the experiment, the validity of the proposed measurement and the feature learning model is verified on the Market1501, CUHK03 database. Experimental results show that the proposed metric performs better than the Mahalanobis distance metric 6.59%, and the proposed feature learning algorithm also achieves good performance. The average precision of the algorithm is improved significantly compared with the existing advanced algorithms.
Integrated Enhancement Algorithm for Hazy Image Using Transmittance as Weighting Factor
Julang JIANG, Wei SUN, Zhendong WANG, Zhu ZHU, Jiangyun ZHENG
2018, 40(10): 2388 -2394   doi: 10.11999/JEIT171032
[Abstract](170) [FullText HTML](59) [PDF 2741KB](23)
Hazy image enhancement has important practical significance. Since the existing haze removal algorithms have disadvantages in improving the global contrast of images, a novel hazy image enhancement algorithm is presented by integrating advantages of haze removal and histogram equalization. Firstly, the hazy image is processed respectively using guided filtering-based dark channel prior algorithm and HSV space-based histogram equalization algorithm. Then, the output image is obtained by fusing the above two results using weighting factor which is constructed by the revised transmittance map. Simulation results show that the algorithm has higher standard deviation, average gradient and information entropy than the present hazy removal algorithm, and shows better result of global and local contrast. The running time of the algorithm mainly depends on the process of haze removal, which can meet the real-time requirements for normal size image.
Human Action Recognition via Spatio-temporal Dual Network Flow and Visual Attention Fusion
Tianliang LIU, Qingwei QIAO, Junwei WAN, Xiubin DAI, Jiebo LUO
2018, 40(10): 2395 -2401   doi: 10.11999/JEIT171116
[Abstract](135) [FullText HTML](72) [PDF 1517KB](12)
Inspired by the mechanism of human brain visual perception, an action recognition approach integrating dual spatio-temporal network flow and visual attention is proposed in a deep learning framework. First, the optical flow features with body motion are extracted frame-by-frame from video with coarse-to-fine Lucas-Kanade flow estimation. Then, the GoogLeNet neural network with fine-tuned pre-trained model is applied to convoluting layer-by-layer and aggregate respectively appearance images and the related optical flow features in the selected time window. Next, the multi-layered Long Short-Term Memory (LSTM) neural networks are exploited to cross-recursively perceive the spatio-temporal semantic feature sequences with high level and significant structure. Meanwhile, the inter-dependent implicit states are decoded in the given time window, and the attention salient feature sequence is obtained from temporal stream with the visual feature descriptor in spatial stream and the label probability of each frame. Then, the temporal attention confidence for each frame with respect to human actions is calculated with the relative entropy measure and fused with the probability distributions with respect to the action categories from the given spatial perception network stream in the video sequence. Finally, the softmax classifier is exploited to identify the category of human action in the given video sequence. Experimental results show that this presented approach has significant advantages in classification accuracy compared with other methods.
q -affine Projection Algorithm and Its Steady-state Mean Square Convergence Analysis
Shiyuan WANG, Chunfen SHI, Yunxiang JIANG, Wenyue WANG, Guobing QIAN
2018, 40(10): 2402 -2407   doi: 10.11999/JEIT171125
[Abstract](207) [FullText HTML](117) [PDF 1044KB](36)
The q-gradient is a generalized gradient based on the q-derivative concept. To improve the filtering performance of the Affine Projection Algorithm (APA), the q-gradient is applied to APA based on the minimum of the recent mean square errors, generating a novel q-Affine Projection Algorithm (q-APA). The q-APA with appropriate setting of q achieves desirable filtering performance in the presence of Gaussian noises. A sufficient condition for guaranteeing convergence of the proposed q-APA is also presented, and its steady-state Excess Mean Square Error (EMSE) of q-APA is obtained theoretically to evaluate the filtering performance. In addition, the Variable q-APA (V-q-APA) is developed to improve further the filtering performance. Simulations in the context of system identification demonstrate the superior filtering performance of the proposed algorithms compared with APA and Variable q-Least Mean Square (V-q-LMS) algorithm in the presence of Gaussian noise.
SL0 Reconstruction Algorithm for Compressive Sensing Based on BFGS Quasi Newton Method
Na SUN, Jiwen LIU, Dongliang XIAO
2018, 40(10): 2408 -2414   doi: 10.11999/JEIT170813
[Abstract](71) [FullText HTML](32) [PDF 1940KB](5)
Smoothed l0 norm (SL0) algorithm is a compressive sensing reconstruction algorithm based on approximate l0 norm, which uses the steepest descent method and gradient projection principle, by selecting a decreasing sequence to get the optimal solution. It has the advantages of high matching degree, low computational complexity and without knowing the signal sparsity. However, the iterative direction of steepest descent method is negative gradient direction, which leads to the " sawtooth phenomenon” and the slower convergence speed in the vicinity of the optimal solution. The Newton method has a good convergence speed but has higher requirement of the initial value and needs to calculate the Hessian matrix. The quasi Newton method overcomes this shortcoming and uses BFGS formula to calculate the approximate matrix of the Hessian matrix, it only needs the first derivative information. On the basis of SL0 algorithm and BFGS quasi Newton method, an improved reconstruction algorithm for Compressed Sensing (CS) signal is proposed. The steepest descent method is first used to get an estimated value, and then is taken as the initial value of quasi Newton method, using BFGS method to update the iterative direction until retaining the optimal solution. The simulation results show that the proposed algorithm has great improvement in reconstruction accuracy, peak signal to noise ratio and reconstruction matching degree.
Gain and Phase Calibration Algorithm of Near-field Source Based on Instrumental Sensors
Mengyu NI, Hui CHEN, Song XIAO, Liuliu NI, Jiajia ZHANG
2018, 40(10): 2415 -2422   doi: 10.11999/JEIT180032
[Abstract](122) [FullText HTML](47) [PDF 1779KB](2)
In order to solve the problem of near-field source localization and array gain-phase error calibration, a method of gain-phase error calibration is proposed based on uniform array symmetry. The distance parameter is separated by reconstructing the virtual array, and then the decoupling between azimuth and error is realized by transforming the steering vector of the virtual array. Through the transformation of the real array steering vector, the decoupling between the distance and the gain-phase error is realized, and the cascade estimation of the azimuth and distance of the near-field source and the gain-phase error coefficient of the array is achieved. The simulation results show that compared with the exist algorithms, the proposed algorithm has less computational complexity, more accurate azimuth and distance parameters estimation, and higher accuracy of gain and phase error calibration.
Robust Capon Beamforming for Towed Array Sonar During Maneuvering
Liankun BO, Xiaoyong ZHANG, Jinyu XIONG
2018, 40(10): 2423 -2429   doi: 10.11999/JEIT180022
[Abstract](106) [FullText HTML](35) [PDF 1760KB](4)
The robust beamformer suffers performance degradation due to the distortion of towed array shape caused by the maneuverings of tow platform. To address this problem, a low complexity robust Capon beamforming method is proposed based on time-varying array focusing and dimension reduction. First, the array shape is estimated sequentially using the array heading data based on Water-Pulley model. The Sample Covariance Matrix (SCM) at each recording time is focused to a reference array model via the STeered Covariance Matrix (STCM) technique to eliminate the array model error. Then, the reducing transform matrix is formed based on the conjugate gradient direction vectors of the focused SCM. The reduced-dimension Capon beamformer is finally derived to calculate the spatial spectrum. The results of the simulations show that, the proposed method can improve the Signal-to-Interference-plus-Noise Ratio (SINR) of the beamforming during the maneuvering of towed array. The results of sea-trial data processing show that the proposed method can improve the output Signal-to-Noise Ratio (SNR) of the target, as well as the performance of detecting weak targets and solving the left-right ambiguity during maneuvering.
A Suppression Algorithm of Blanket-distance Deception Compound Jamming based on Joint Signal-data Processing
Guohong WANG, Jie BAI, Dianxing SUN, Xiangyu ZHANG
2018, 40(10): 2430 -2437   doi: 10.11999/JEIT170759
[Abstract](166) [FullText HTML](34) [PDF 2168KB](7)
Considering at the problem that the suppression effect of single signal processing or data processing is poor on blanket-deception compound jamming, a suppression algorithm of blanket-distance deception compound jamming based on joint signal-data processing is proposed. Firstly, the Fractional Fourier Transform (FRFT) domain narrowband filtering and LFM signal reconstruction algorithm are used to suppress the suppression of the signal layer and reduce the leakage probability of the real target. Then, the target tracks and the deception tracks are rejected by using the M/N logic method. Finally, according to the different characteristics of the angle variance between the false targets and the true targets, the false targets are eliminated by the \begin{document}${\chi ^2}$\end{document} test and the clustering algorithm. The simulation results verify the good effect of the algorithm proposed in this paper.
Strobe Pulse Design for Quadrature Multiplexed Binary Offset Carrier Modulation in BeiDou B1C Signal
Wengang LI, Chen HUANG, Yiwei WANG
2018, 40(10): 2438 -2446   doi: 10.11999/JEIT180109
[Abstract](14) [FullText HTML](12) [PDF 2692KB](2)
The third generation of BeiDou satellite navigation system employs Quadrature Multiplexed Binary Offset Carrier (QMBOC) modulation for B1C signal. In order to improve the anti-multipath performance of code tracking loops and solve the problem of code tracking ambiguity for BeiDou system, a double strobe code tracking loop structure for QMBOC(6, 1, 4/33) modulation is proposed. According to the ideal phase discrimination function and the auto-correlation function of BOC signal, two kinds of strobe pulse are designed for BOC(1, 1) and BOC(6, 1) respectively. Then, these two strobe pulse waveforms are used to correlated with the input signal in the code tracking loop. Finally, these two correlation functions are weighed combined for phase discrimination. The computer simulation results show that the proposed method can not only eliminate the code tracking ambiguity for QMBOC(6, 1, 4/33), but also improve the anti-multipath performance dramatically: the multipath error envelope area is reduced by about 33% compared with the existed method.
Maneuvering Decision-making Method of UAV Based on Approximate Dynamic Programming
Changqiang HUANG, Kexin ZHAO, Bangjie HAN, Zhenglei WEI
2018, 40(10): 2447 -2452   doi: 10.11999/JEIT180068
[Abstract](124) [FullText HTML](80) [PDF 1258KB](12)
To solve the problem of dimension disaster when solving air combat maneuvering decision-making by dynamic programming, a swarm intelligence maneuvering decision-making method based on the approximate dynamic programming is proposed. Firstly, the Unmanned Aerial Vehicle (UAV) dynamic model and advantage functions of situation are established. On this basis, air combat process is divided into several stages according to dynamic programming thought. In order to reduce the search space, an Artificial Potential Field (APF) Guiding Ant Lion Optimizer (ALO) approximate optimal control amount is adopted in each programming stage. Finally, by comparing expert system, the experiment result indicates that the high dynamic and real-time air combat maneuvering decision can be solved by the proposed method effectively.
Visual Objects Detection Based Robust Ridge Regression Indoor Localization Method
Haowei XU, Baowang LIAN, Xiaojun ZOU, Zhe YUE, Peng WU
2018, 40(10): 2453 -2460   doi: 10.11999/JEIT170876
[Abstract](63) [FullText HTML](33) [PDF 2349KB](3)
The indoor vision positioning algorithm based on object detection is a novel indoor positioning solution, which determines the position of the user through the process of objects detection, position matching, location equation calculation, etc. However, limited by the field-of-view of monocular camera and objects detection accuracy, the localization equation, which is constructed according to the detected objects range information, is seriously ill conditioned. Therefore, this paper proposes a novel localization method based on an improved robust ridge regression estimation, which reduces the influence of the lower accurate observations by iterative weight selection. The experimental results show that compared with Ordinary Least Square (OLS), Levenberg-Marquardt (LM) and Ridge Regression (RR) algorithms, the proposed improved robust ridge regression estimation algorithm can effectively improve the positioning success rate and positioning accuracy of the object detection based indoor navigation method.
Polarimetric SAR Coherent Change Detection Method Based on Volume Scattering Constraint
Guangyu JI, Xingdong LIANG, Yongwei DONG, Yanlei LI
2018, 40(10): 2461 -2469   doi: 10.11999/JEIT180035
[Abstract](199) [FullText HTML](95) [PDF 9277KB](21)
Coherent Change Detection (CCD) detects change areas in the scene using its decorrelation, yet vegetation areas with volume scattering and low signal-noise ratio areas in the scene also appear as low coherence, which causes interference to change areas to be detected. A polarimetric SAR CCD method is proposed. Firstly, the polarimetric coherence between two SAR images before and after changing is employed to set up weighted trace coherence statistics. Secondly, the polarimetric coherence between channels of each SAR image is employed to set up volume scattering constraint by establishing GEV mixture distribution model and solving parameters of each part using improved EM algorithm. Lastly, constraint of scattering power change is combined to set up the final polarimetric CCD test statistics. Using this method, the interference could be eliminated without influence of detect performance. The method is validated by two L-band full-polarimetric SAR images before and after changing. Results and index parameters demonstrate the correctness and validity of the proposed method.
Investigation on PRI Variation for High Squint Spaceborn Spotlight SAR
Pei WANG, Wei XU, Ning LI, Weidong YU
2018, 40(10): 2470 -2477   doi: 10.11999/JEIT180049
[Abstract](89) [FullText HTML](43) [PDF 4025KB](7)
High squint spotlight mode of spaceborne SAR can be used to achieve high resolution and wide swath, and also can be used to acquire information of target from multi-azimuth. However, the considerable range migration can result in efficiency decreasing in data acquisition, and dilemma in system design. This problem can be solved by the technology named PRI (Pulse Repetition Interval) variation which can track the slant range variation of the target during data acquisition. In this paper, the principle of PRI variation is studied, and methods of PRI sequence iterative design and system parameter selection are proposed. Two approaches to reconstruct the nonequal spaced and nonperiod data in azimuth sampling are compared. Finally, the first results of PRI variation mode of airborne SAR experiment with high slant spotlight mode are presented.
Hyperspectral Image Compression Based on Adaptive Band Clustering Principal Component Analysis and Back Propagation Neural Network
Shanxue CHEN, Yanqi ZHANG
2018, 40(10): 2478 -2483   doi: 10.11999/JEIT180055
[Abstract](89) [FullText HTML](44) [PDF 1069KB](8)
Hyperspectral remote sensing images have a wealth of spectral information and a huge universe of data. In order to utilize effectively hyperspectral image data and promote the development of hyperspectral remote sensing technology, a hyperspectral image compression algorithm based on adaptive band clustering Principal Component Analysis (PCA) and Back Propagation (BP) neural network is proposed. Affinity Propagation (AP) clustering algorithm for adaptive band clustering is used, and PCA is performed on the each band group respectively after clustering. Finally, all principal components are encoded and compressed by BP neural network. The innovation point lies in BP neural network compressed image during the training step, the error of backpropagation is to compare difference between the original image and the output image, and then adjust the weight and threshold of each layer in the reverse direction. Band clustering of hyperspectral images can not only effectively utilize the spectral correlation and improve the compression performance, but also reduce the computational complexity of PCA. Experimental results investigate that the proposed algorithm achieve a better performance on Signal-to-Noise Ratio (SNR) and spectral angle than other algorithm under the same compression ratio.
Resolution Enhancement Method for Bistatic ISAR One-dimensional Range Profile Under Low SNR
Wenfeng CHEN, Mingjiu LÜ, Saiqiang XIA, Long XIANG, Jun YANG, Xiaoyan MA
2018, 40(10): 2484 -2490   doi: 10.11999/JEIT180081
[Abstract](69) [FullText HTML](10) [PDF 1900KB](4)
To solve the problem of declined resolution of Bistatic Inverse Synthetic Aperture Radar (B-ISAR) imaging by bistatic angle, a B-ISAR range profile resolution enhancement algorithm is put forward based on Multiple Measurement Vector (MMV) Complex Approximate Message Passing (MCAMP). The range joint sparse model is established. By utilizing vectorization operation, the joint sparse problem is converted into a block complex basis pursuit denoising problem. To achieve the range profile which is immune to bistatic angle influence, the MCAMP algorithm is proposed by using the Kronecker product. The Fast Fourier Transform (FFT) is introduced to instead of multiplication between matrix and matrix, which improves the efficiency of the proposed algorithm by reducing the computational complexity further. Simulation imaging results verify the effectiveness and efficiency of the proposed method.
Study on Multi-target Angle Tracking Algorithm of Bistatic MIMO Radar with Unknown Target Number
Zhengyan ZHANG, Jianyun ZHANG, Qingsong ZHOU
2018, 40(10): 2491 -2497   doi: 10.11999/JEIT171174
[Abstract](147) [FullText HTML](55) [PDF 1459KB](16)
In order to solve the angle tracking problem of bistatic MIMO radar when the number of target is unknown, a joint tracking algorithm of the number of target and the angle is proposed. There is no variable in Adaptive Asymmetric Joint Diagonalization (AAJD) algorithm that can directly represent the eigenvalue. Therefore, the idea of principal component sequence estimation is introduced to the improved AAJD algorithm, and the eigenvalues are iteratively evaluated. Then, the number of target is estimated by using the improved information theory. Secondly, the anti-dithering algorithm of target number is proposed, which improves the robustness of the algorithm. Finally, the ESPRIT algorithm is improved to realize the automatic matching and association of DOD and DOA. The simulation results show that the improved AAJD algorithm can successfully track the number of target and angle trajectories. The efficiency of the proposed method is verified.
Research on Characteristics and Suppression Methods of Side Peaks of Passive Radar Based on LTE Signal
Xiaode LÜ, Hanliang ZHANG, Jingmao YANG, Qi YUE
2018, 40(10): 2498 -2505   doi: 10.11999/JEIT180019
[Abstract](104) [FullText HTML](61) [PDF 2738KB](7)
For the passive radar based on Long Term Evolution (LTE) signal, firstly, the ambiguity function are analyzed, and the producing mechanism of different side peaks are explained. Then, a series of corresponding suppression algorithms are proposed for two types of side peaks degrading detection performance: For the side peaks caused by the cyclic prefix, a fast ambiguity algorithm based on non-continuous chunking of data is proposed; For the side peaks caused by the non-continuous spectrum, the suppression algorithm of bandwidth synthesis and frequency domain windowed is proposed. Finally, the thumbtack ambiguity function of the LTE signal is obtained through integrated processing of two suppression algorithms. The work of this paper provides a new method for the side peaks suppression of the passive radar based on the LTE signal.
Clutter Suppression Method for Short Range Slow Moving Target Detection
Lin ZHENG, Weiwei YAO, Chao YANG, Hongbing QIU
2018, 40(10): 2506 -2512   doi: 10.11999/JEIT180031
[Abstract](147) [FullText HTML](84) [PDF 1772KB](22)
This paper proposes a method of clutter suppression based on phase encoding and subspace projection for close slow-moving target detection in strong clutter environment. In the framework, the periodic detection signal is modulated with phase encoding, and the clutter is whitened through echo decoding of the slow-time dimension to reduce the correlation between clutter and target echo. Furthermore interference subspace is constructed on the basis of the autocorrelation differences between whitened clutter and useful signal components. The receiving signal is projected to the signal subspace orthogonal to the clutter subspace for clutter suppression. Since the construction of clutter space does not need to assume the clutter model, it avoids the problem of mismatch between the model hypothesis and the actual environment. Simulation results and real data processing results show that this method has better performance than conventional methods under low signal-to-clutter ratio.
Scheduling Service Staffs for Alien Airlines Using Block Gibbs Sampling
Min LU, Li WANG, Ling TANG
2018, 40(10): 2513 -2520   doi: 10.11999/JEIT180181
[Abstract](122) [FullText HTML](76) [PDF 760KB](11)
Scheduling staffs servicing alien airlines aims to yield task-person assignments by covering the required skills and minimizing employee total working hours as well as balancing staffs’ workload. Its essence is a personnel scheduling problem constrained by multiple task types, hierarchical skills as well as day and night alternation. The existing algorithms do not consider the constraint of day and night alternation. An algorithm is proposed to address that issue. The proposed algorithm firstly designs a data copy trick to quickly model the issue of staff scheduling constrained by day and night alternation. A novel Block Gibbs sampling technique with replacement is designed to efficiently optimize the formulated problem. Theoretical analysis indicates that the computational complexity of the proposed algorithm is the same scale to that of the baselines, whereas the proposed algorithm gains high sampling efficiency. Experimental results on a real dataset shows the improvement of the proposed algorithm over the existing methods is at least 0.62% in terms of evaluation measures.
Cat Swarm Optimization Task Scheduling Algorithm Based on Double Arbitration Mechanism and Taguchi Orthogonal Method
Xingming ZHANG, Congyue YIN, Shuai WEI, Shengzhao YE, Ping LÜ
2018, 40(10): 2521 -2528   doi: 10.11999/JEIT180215
[Abstract](240) [FullText HTML](138) [PDF 2496KB](39)
To solve communication conflicts and algorithm running time problem in task scheduling process of heterogeneous computing system, a cat swarm optimization task scheduling algorithm is proposed based on double arbitration mechanism and Taguchi orthogonal method. Firstly, the double arbitration mechanism is used to manage the task resources, and the task assignment is dynamically decided to avoid effectively communication conflicts. Then, the Taguchi orthogonal method is applied to the tracking mode of the cat swarm optimization process to reduce the algorithm running time and improve the quality of the solution. Experimental results show that the algorithm runs at a rate of at least about 10% faster than other algorithms. The algorithm performs best in parallelism when dealing with a large number of tasks and has considerable advantages in heterogeneous environments.
Calculation of Electromagnetic Propagation Characteristics over Rough Sea Surface Based on Double-layer Model
Mengda CUI, Hao CHA, Bin TIAN, Qun WANG
2018, 40(10): 2529 -2534   doi: 10.11999/JEIT171020
[Abstract](106) [FullText HTML](46) [PDF 898KB](3)
A double-layer model is proposed to reduce the calculation amounts of the Linear Ship Map (LSM) model. The proposed model can be used for rapid and accurate calculation of the electromagnetic propagation characteristics in the complicated atmospheric environment over the sea. In the proposed model, the calculation regions are divided into the upper-layer and the lower-layer. The upper-layer is calculated by the Wide angle Parabolic Equation (WPE) model and the lower-layer is calculated by the LSM model. Through reducing the calculation height and optimizing the step length, the proposed model can be exact and rapid. By simulation, the proposed model is compared with LSM model in the smooth and the rough sea surface conditions. The results show that the proposed model can decrease the calculation time by 1/10 in the rough sea surface condition.
Study on Electron Emission Phenomenon of the Surface Micro Area of Coated Impregnated Dispenser Cathode
Shengyi YIN, Feng REN, Zhipeng LU, Yongqing ZHANG, Shenjin ZHANG, Feng YANG, Dong WEI, Jiao HAN, Yang LI
2018, 40(10): 2535 -2540   doi: 10.11999/JEIT171000
[Abstract](166) [FullText HTML](42) [PDF 1815KB](2)
The impact on impregnated cathode electronic emission, when it is coated by films, is an important studied content in the field of thermionic cathode. A cathode sample is evenly split by impregnated dispenser cathode and coated impregnated dispenser cathode. The sample is activated at 1150 ℃ in the Deep UltraViolet laser Photo- and Thermal- Emission Electron Microscope system (DUV-PEEM/TEEM) for 2 hours. In this system, the micro- electron emission phenomenon of two cathode and its changes with temperature are compared and studied directly. The results show that the electronic emission of both impregnated cathode and coated impregnated cathode are mainly located at the pores and the edge of the adjacent particles; Along with the rise of cathode's temperature, the cathode emission without coating anything is still mainly focused on the pores and nearby narrow regions on cathode surface, that the emission area changed a little. However, for the coated impregnated dispenser cathode, the effective emission area is extended from the pores and its edge to the area far away from the pores. These results for the first time give the electron emission characteristics for impregnated cathode coated with film, which have certain reference value to understand the emission mechanism of this cathode.

Monthly Journal Founded in 1979

The Source Journal of EI Compendex The Source Journal of ESCI Database

Competent unit:Authorized by CAS

Host unit:Hosted by IECAS,Department of Information Science of NNSFC

Editor-in-Chief:Yirong Wu

ISSN 1009-5896  CN 11-4494/TN

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