In conventional cooperative spectrum sensing, the spectrum sensing 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 performance, compared to the non-OPC aided cooperative spectrum sensing schemes.
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.
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.
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.
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.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Conventionally, the physiological monitoring system obtains singnal by electrode or bandage which is connected with skins and has disadvantages such as: uncomfortable and bad compliance to users. In order to overcome those problems, a new physiological monitoring system, which is based on the principle that micro bend of optical-fiber induced by weak movement of physiology can change the light intensity to get BallistoCardioGram (BCG) signal, is developed. In such system, the respiration rate, heart rate and body movement are obtained by self-adaption detecting the tiny variation of light intensity. In order to protect fiber and enhance the stability and reliability of system, the fiber is embedded into mattress or cushion with a sandwich structure. Simultaneously, it makes the system have high sensitivity that the fiber is uniformly routed with serpentine-curve shape in the middle of mattress or cushion. It is illustrated by the measurement in several hospitals that the mean error of heart rate is –0.26±2.80 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9984 to the standard values. It is exhibited as well that the mean error respiration rate is 0.41±1.49 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9971 to the standard values. It is suggested that the developed system can be senselessly used under zero load and is promised in future.
To control the initial azimuth position and the magnitude of the false moving scene, a false moving scene jamming method against Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) is proposed based on double jammers and magnitude modulation. The identical false scenes can be generated in the desired position by the two jammers using delay and shift-frequency modulation. To control the initial azimuth position of the false moving scene, the magnitude ratio of the double scenes are set by controlling the phase of each false target which is generated after two false scenes are superimposed. Theoretical analysis shows that the false moving scene can be generated with the initial azimuth position and magnitude controllable. Next, the jamming effects and influencing factors are analyzed, and the application model of interference algorithm is established. Subsequently, the setting methods of the imaging position, the initial azimuth position and the magnitude compensation coefficient are presented. The jamming effect of the distance between the two jammers in azimuth is also analyzed. Finally, the validity of the proposed method is verified by simulation experiences.
The traditional method used to extract micro-motion is based on the envelope information of the wideband echo range profile, the estimation accuracy of the traditional method is unsatisfactory. To deal with this problem, a new method for parameter estimation of micro-motion feature is proposed, which is implemented by combining envelope information and phase information of the wideband echo range profile. Firstly, the Keystone transform is performed to each segment obtained by segmenting the envelope of the echo range profile to estimate micro-motion coarsely. Then, the echo phase information is extracted according to the coarse estimation results. The accurate micro-motion curve of each scattering point can be obtained by the principle of phase-derived range. Finally, the estimation of the micro-motion and geometric parameters of the precession target is completed by utilizing the extracted micro-motion curve. Compared with the traditional method, the proposed algorithm can improve the precision of parameter estimation effectively. The effectiveness and stability of the proposed algorithm is verified by simulation experiments.
For a maximal planar graph G, the operation of extending 3-wheel is a process from G to G∨v, where v is a new vertex embedded in some triangular face xyz of G and G∨v is a graph of order |V(G)|+1 obtained from G by connecting v to each one of x, y, z with one edge. A recursive maximal planar graph is a maximal planar graph obtained from K4 by extending 3-wheel continuously. A (k,l)-recursive maximal planar graph is a recursive maximal planar graph with exactly k vertices of degree 3 so that the distance between arbitrary two vertices of degree k is l. The existence of (k,l)-recursive maximal planar graph is discussed and the structures of (3,2)-as well as (2,3)-recursive maximal planar graphs are described.
Information geometry based matrix Constant False Alarm Rate (CFAR) detector is an efficient solution to the intractable issue of target detection for K-distributed sea clutter environment. However, most existing matrix CFAR detectors cost heavy computation complexity, which leads to a limitation in practical application. Based on the Neyman-Pearson criterion, the Likelihood Ratio Test (LRT) is analyzed, the relationship between LRT statistic and the Maximum Eigenvalue is derived, and Matrix CFAR Detection method based on the Maximum Eigenvalue (M-MED) is designed. Simulation results verify that the proposed method can achieve better detection performance with relatively lower computational complexity.
In order to solve the problem of Direction Of Departure (DOD), Direction Of Arrival (DOA) and Doppler frequency estimation in bistatic MIMO radar, a low complexity method is proposed for joint estimation of the three parameters based on the multi-dimensional Vandermonde structure characteristic of the parameter manifold matrices. First, a third-order tensor is constructed according to the multi- dimensional structure of the echo model. Three equivalent matrices are obtained by cutting the tensor along transmit dimension, receive dimension and pulse dimension respectively. Then, combining the multi-dimensional Vandermonde characteristic with the Khatri-Rao product characteristic of the left-singular matrix of the equivalent matrix, transmit manifold matrix, receive manifold matrix and Doppler manifold matrix are estimated. Finally, the DOD, DOA and Doppler frequency are estimated by Root-MUSIC algorithm. Compared with the existence methods, the proposed algorithm improves obviously the estimation precision, and its computational cost is comparable to that of Estimation of Signal Parameters via Rotation Invariant Techniques (ESPRIT) method in small pulse number. The effectiveness of the proposed method is verified by simulation results.
To solve the problem of radiant target localization using Time Difference Of Arrival (TDOA) measurements from multiple sensors, an algebraic closed-form method based on Weighted Least Squares (WLS) minimizations is proposed, with the priori knowledge of target altitude. In near distance scenario, neglecting the effect of earth curvature, the target altitude can be regarded as one-dimensional coordinate of the target. Based on this condition, the target position is solved by a new two-step WLS algorithm. It does not require initial solution guess, and is computationally attractive due to the non-iterative operation. Simulation results show that the target localization accuracy is greatly improved using target altitude, and the proposed method can reach Cramer-Rao Lower Bound (CRLB) accuracy under small Gaussian measurement noise.
In order to overcome difficulty in extending flank array by traditional Extended Towed Array Measurement (ETAM) technique based on curvilinear maneuverability of Autonomous Underwater Vehicles (AUV), a new ETAM method is proposed by combining estimating phase correction factors in beam domain and linear fitting motion compensation in element domain. The method estimates phase differences in beam domain and implements linear fitting of phase correction factors in element domain to compensate phase error due to curvilinear motion, which can acquire accurate phase correction factors and extend array. Results of simulation and experiment show that no matter straight line navigation or curvilinear maneuverability, the method achieved higher DOA estimation accuracy, angular resolution and signal gain, which is also suitable for multi-target resolving. Especially in low signal-noise-ratio condition, the improvement of this method in performance is more obvious, for which it has strong practicality and environmental tolerance.
In order to identify the red signal in the conduction leakage signal of the display power line effectively, a Particle Swarm Optimization-Support Vector Mechine (PSO-SVM) algorithm based on Particle Swarm Optimization (PSO) algorithm for parameter optimization is proposed. Firstly, the conducted leakage signal is filtered, then the PSO-SVM is used to train and classify the conducted leakage signals and compared with the SVM classification. Finally, the display image is reconstructed using PSO-SVM. The result shows that the the red signal can be effectively identified, and the identification rate is significantly higher than the SVM classifier.
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.
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.
Traditional Inverse SAR (ISAR) imaging algorithms neglect the impact of high-order rotational phase in the signal, which may make ISAR images of a target defocused. Further, the size of a target can not be obtained from ISAR image directly. In this study, an effective method to achieve the rotation compensation and cross-range scaling for ISAR imaging is proposed. Firstly, all the signals of the target are used to form the Local Average Doppler Trend (LADT) signal. Subsequently, RANdom SAmple Consensus (RANSAC) algorithm is performed to estimate the Doppler rate and effective rotational velocity. Finally, high-precision rotation compensation and cross-range scaling can be accomplished. Simulation and real data experiments validate the effectiveness of the proposed method.
The visual-based scene recognition and localization module is widely used in vehicle safety system. This paper proposes a new method of scene recognition based on local key region and key frame, which is based on the problem of large amount of training data, large matching complexity and low tracking precision. The proposed method meets the real-time requirements with high accuracy. First, the method uses the unsupervised method to extract the significant regions of the single reference sequence captured by the monocular camera as the key regions. The binary features with low correlation in key regions are also extracted to improve the scene matching accuracy and reduce the computational complexity of feature generation and matching. Secondly, key frames in the reference sequence are extracted based on the discrimination score to reduce the retrieval range of the tracking module and improve the efficiency. Practical field tests are done on real data of the light railway system in Hong Kong and the open test data set in Nordland. The experimental results show that the proposed method achieves fast matching and the precision is 9.8% higher than SeqSLAM which is based on global feature.
To meet the personal quality requirement of video streaming service under the access and backhaul integrated small base station scene, a user satisfaction maximization algorithm is proposed. The algorithm adjusts dynamically the spectrum resources used for next-cycle queue transmission by analysis the mismatch degree between the actual system reachable rate and user satisfaction demand rate. The corresponding optimization model of the quality satisfaction of all users is established. Then, the Lyapunov stochastic optimization method is used to transform the initial problem into drift plus penalty, the overflow probability constraint is transformed into inequality of variables. Finally, using the proposed user access bandwidth allocation algorithm based on Lagrange dual decomposition and the backhaul and access bandwidth allocation algorithm based on interior point method. The simulation results show that the algorithm can improve the quality satisfaction of all users and ensure the system stability.
Radio-Frequency (RF) energy harvesting technique is proved a promising way to solve the short network lifetime issue of traditional Wireless Sensor Networks (WSNs) caused by sensors' finite energy. In the existing charging schemes using the mobile RF Energy Transmitter (ET), ET can move to any location along any moving direction in the monitoring area for energy provision. However, in the practical scenario, ET can only move along the existing roads. For the first time, the charging time minimization issue is considered under the moving trajectory constraint. The Mobile Charging (MC) scheme where ET transmits RF energy while moving and the Static Charging (SC) scheme where ET transmits RF energy while unmoving are proposed, whose the moving trajectory and energy provision time are optimized by the proposed efficient algorithms. Simulation results reveal that the total charging delay of proposed schemes is smaller than the baseline scheme of transmitting energy at turning points. The MC scheme has lower computational complexity but has a slightly larger charging delay as compared to the SC scheme.
As a key part in Non-Orthogonal Multiple Access (NOMA), user grouping is of particular importance for non-orthogonal multiple access system to improve throughput performance and user fairness. When the number of users and the available resources is increased, the optimal scheduling of user grouping will be infeasible, so a multi-user grouping optimization algorithm for different sub-bands is proposed. According to the user channel gain difference and the restrictions of the multiplexed user number in the same subband, the proposed algorithm firstly performs the process of the initial multi-user grouping to reduce the user’s search space. Then, the optimized combination of the initial grouping users is gradually completed, and the maximum geometric mean user throughput is used as user grouping criterion, which can further enhance the cell-edge user throughput. Simulation results show that the system total throughput and geometric mean user throughput performance of the proposed algorithm can be improved by more than 3% compared with the traditional user grouping algorithms.
Fully homomorphic encryption allows any operation evaluation on encrypted data without decryption. The existing integer-based homomorphic encryption schemes are designed only for two participants namely one party encryption one party decryption (one-to-one), whose computational efficiency is generally low, plaintext space is small, so it can not be applied to big data, cloud computing and other actual scene. Therefore, a full homomorphic encryption scheme with multi-party encryption, one party decryption (multiple to one) is presented. The scheme simplifies the key generation process on the basis of guaranteeing the security, but also gives the range of the number of encrypted parties that can be decrypted accurately in the process of homomorphic operation. Meanwhile, in the random oracle model, the security of the new scheme is proved based on approximate Greatest Common Divisor (GCD) problem. Numerical analysis demonstrates that the presented scheme can not only extend the data traffic, but also improve the efficiency by comparing with the existing schemes. Simulation results show that proposed scheme is more practical in the range of integer, and meets the requirements of the users to the system response. Finally, the plaintext space is expanded to 3 bit, comparing and analysing the experiment with the scheme of 1 bit.
To deal with the lack of a secure and efficient data source authentication mechanism in Software-Defined Network (SDN), a packet forwarding authentication mechanism based on cipher identification is proposed. Firstly, a packet forwarding authentication model based on cipher identification is established, where the cipher identification is identified as a passport of IP packets entering and leaving the network. Secondly, the SDN batch anonymous authentication protocol is designed to decentralize the authentication function of the SDN controller to the SDN switch. The SDN switch performs user authentication and cipher identification verification, and quickly filters forgery, falsification, and other illegal packets to improve the unified authentication and management efficiency of the SDN controller, while providing users with the conditions of privacy protection. Thirdly, a scheme for sampling and verifying packets based on cipher identification in any node is proposed, where any attacker can not bypass the packet detection by inferring the sample, to ensure the authenticity of the packet while reducing its processing delay. Finally, safety analysis and performance evaluation are conducted. The results show that this mechanism can quickly detect packet falsification and tampering and resist ID analysis attacks, but at the same time it introduces about 9.6% forwarding delay and less than 10% communication overhead.
Some problems of multi-user downlink power allocation and beamforming in a underlay Cognitive Radio Network (CRN) with imperfect Channel State Information (CSI) are addressed. They include ignoring the interferences of the Primary Network (PN) to the Secondary Users (SU), conventional SDR algorithm of convex optimization needing the constraint approximation, the high complexity of the algorithm, and implemented with difficulty, etc. Firstly the term of interference of the PN to the SU is added to the CRN model. The optimization problem is formulated with the worst-case imperfect CSI. Next the constraints of the problem are transformed by means of Lagrange duality. Then, based on the form of the problem, the simple, fast and practical iterative algorithm is obtained by utilizing the duality of uplink-downlink, introducing virtual power, and transforming the optimization problem into the problem of uplink power allocation and beamforming. Numerical simulation results show that it converges faster. It is also found that the errors of the imperfect CSI not only influence the downlink power but also change the feasibility region. The variation of transmitting power of the PN Base Station (PBS) could affect the feasibility region notably.
A modified Ensemble Kalman Filter (EnKF) theory model based on kinematic equations is proposed to realize the historical fitting analysis and trajectory prediction of the target trajectory in the multi-source observation data scenario. This model is applied to accurately calculate the target motion state parameters (velocity and acceleration), then the target’s follow-up movement is predicted. The multi-source observation data fusion is realized by using the EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF.
In order to utilize storage space and fetch content effectively in Named Data Networking (NDN), this paper constructs a model for caching problem and proposes a greedy algorithm based on topology information. To optimize the algorithm, content popularity is introduced into execution. Furthermore, content hit distance is shortened effectively. This paper simulates a NDN network based on some real topology data with ndnSIM, and compares the proposed algorithm with traditional prob algorithm, default Cache Everything Everywhere (CEE) algorithm and degree based Heterogeneous Storage Size (HSS) algorithm through simulation. The results show that the algorithm proposed in this paper has better performance.
A fast algorithm based on Frequency Locator Polynomial (FLP) for sparse spectrum recovery is proposed. Using the shifted subsampled signals, the FLPs are constructed, thus to locate rapidly the nonzero frequencies. In particular, the nonlinear problem of sparse spectrum recovery is converted into solving a series of linear equations. Experimental results show that the proposed algorithm exhibits higher processing speed and lower error spectrum reconstruction rate than its predecessor BigBand.
Tracking effects of algorithms using correlation filter are easily interfered by deformation, motion blur and background clustering, which can result in tracking failure. To solve these problems, a visual tracking algorithm based on global context and feature dimensionality reduction is proposed. Firstly, the image patches uniformly around the target are extracted as negative sample, and thus the similar background patches around the target are suppressed. Then, an update strategy based on principal component analysis is proposed, constructing the matrix to reduce the dimensionality of HOG feature, which can reduce the redundancy of feature when it updates. Finally, the color features are added to represent the motion target and the response of the system states are adaptively fused according to the features. Experiments are performed on recent online tracking benchmark. The results show that the proposed method performs favorably both in terms of accuracy and robustness compared to the state-of-the-art trackers such as Staple or KCF. When deformation occur, the proposed method is shown to outperform the Staple tracker and KCF algorithm by 8.3% and 13.1% respectively in median distance precision.
In Software-Defined Networking (SDN) with distributed control plane, the switches are assigned to controllers using only the quantity distribution of flow requests as the basis of resource allocation. To address this issue, the control resource consumption of flow requests processing with different characteristics is analyzed taking the source and destination of flow as an example, from which a conclusion is drawn that the characteristics distribution of flow should be taken into account when allocating control resource. Then, a flow characteristics aware controller assignment model is designed, and a fast algorithm coping with the fluctuation of flow request is proposed. Simulation results show that when solving with the simulated annealing algorithm, the model can save 10%～20% of control resource compared with the load balancing model; with 10% of resource saving, the proposed algorithm outperforms the simulated annealing algorithm in execution speed and scalability.
A relay aided secrecy polar coding method is proposed for the communication systems where the relay uses Decode-and-Forward (DF) in probability. It ensures the transmission reliability and improves the secrecy rate. First, the transmitter encodes the secrecy bits in two layers: the first layer is designed over the virtual Binary Erasure Channel (BEC) that generated by the probabilistic DF relay, and the second layer is designed over the real transmission channels. After receiving the codeword, relay decodes and extracts the frozen bits which the legitimate user can not obtain directly in probability, and re-encodes them by classical secrecy polar coding. Finally, the receiver decodes the received codewords from the relay and the transmitter in turn. The theory and simulation results verify that the legitimate user is able to decode reliable, while the eavesdropper can not obtain any information about the secrecy bits. Moreover, the secrecy rate increases as the code length and the relay forwarding probability increase, and it outperforms the classical secrecy polar coding method.
Atmosphere refraction error is a main error factor to affect synchronization accuracy, tracing accuracy, navigation accuracy and positioning accuracy for various radio systems. On the basis of geometrical optics, the ray tracing method can precisely make this error correction. For the problem that traditional ray tracing method can not deal with the abnormal atmosphere, ray tracing differential form is derived and then a correction method suitable for arbitrary atmosphere is proposed. The tracing data from ground station are taken to test this method. In addition, the refractive error in evaporation duct is compared with that in standard atmosphere. The results show that the refractive error of radio wave trapped entirely in the duct has the biggest influence. The proposed method provides technique support for improving the measurement precision of radio system in arbitrary atmosphere.
The existing hardware task scheduling algorithms describe task imperfectly and ignore the compactness of time dimension. The task downloading time is considered for improving the task attribute, and the 3D-resource model with the two dimensional resource of device and time is established, in order to abstract the issue of task layout into a special three-dimensional space placement issue. With this model, it is concluded that the existing algorithms can not overcome the unpredictability of the task and the diversity of resource occupancy, leading low scheduling success rate and resource utilization rate. To solve the problem, a three dimensional reconfigurable task scheduling algorithm called 3D_RTSA is proposed. A scheduling strategy based on task urgency and a layout strategy based on 3D fragmentation are designed and implemented. Compared with the other 4 algorithms, the results show that the scheduling success rate of 3D_RTSA is 3%, 21%, 28%, 35% higher than that of GC, Look-aheadest, SPSA and DTI algorithms under the condition of heavy load and small task C30, and the utilization ratio of resources is 5% and 18% higher than that of Look-aheadest and SPSA algorithm under the condition of light load and large task C50. Besides, the time complexity of the algorithm is not increased.
Fuzzy clustering is a kind of clustering algorithm which shows superior performance in recent years, however, the algorithm is sensitive to the initial cluster center and can not obtain accurate results of clustering for the boundary samples. In order to improve the accuracy and stability of clustering, this paper proposes a novel approach of fuzzy clustering ensemble model based on distance decision by combining multiple fuzzy clustering results. First of all, performing several times clustering for data samples by using FCM (Fuzzy C-Means), and corresponding membership matrices are obtained. Then, a new method of distance decision is proposed, a cumulative distance matrix is constructed by the membership matrices. Finally, the distance matrix is introduced into the Density Peaks (DP) algorithm, and the final results of clustering are obtained by using the improved DP algorithm for clustering ensemble. The results of the experiment show that the clustering ensemble model proposed in this paper is more effective than other classical clustering ensemble model on the 9 data sets in UCI machine learning database.
A novel one-dimensional discrete chaotic criterion is firstly constructed by studying the modular operation of the discrete dynamical systems. The judgement of the Marotto theorem is used to prove that the suggested dynamical systems are chaotic. Secondly, several special chaotic systems satisfied with the conditions of this paper are given, and the bifurcation diagram and Lyapunov exponential spectrum are also analyzed. Numerical simulations show that the proposed chaotic systems have the positive Lyapunov exponent, which indicates the accuracy of the proposed theory. Additionally, a Pseudo-Random Number Generator (PRNG) is also designed based on the given new chaotic system. Using SP800-22 test suit, the results show that the output sequence of PRNG has good pseudorandom. Finally, as an application of the PRNG, an image encryption algorithm is given. The proposed encryption scheme is highly secure Key space of 2747 and can resist against the statistical and exhaustive attacks based on the experimental results.
In order to reduce the computational complexity of computing unknown syndromes for the coefficients of the error-locator polynomial and reduce the decoding time when one is decoding, this paper proposed an algebraic decoding algorithm of (41, 21, 9) QR code without calculating the unknown syndromes by solving the Newtonian identity. Simultaneously, an objective theoretical analysis of the computational complexity is given for the part of improvement. Besides, this paper also puts forward the simplifying conditions to determine the number of errors in the received word, which in order to further reducing the decoding time. Simulation results show that the proposed algorithm reduces the decoding time with maintaining the same decoding performance of Lin’s algorithm.
Gaussian kernel is usually used as the similarity measure in spectral clustering algorithm, and all the available features are used to construct the similarity matrix with Euclidean distance. The complexity of the data set would affect its spectral clustering performance. Therefore, an improved spectral clustering algorithm based on Axiomatic Fuzzy Set (AFS) is proposed. Firstly, AFS algorithm is combined to measure the similarity of more suitable data by recognizing features, and the stronger affinity matrix is generated. Then Nyström sampling algorithm is used to calculate the similarity matrix between the sampling points and the sampling points and the remaining points to reduce the computational complexity. Finally, the experiment is carried out by using different data sets and image segmentations, the effectiveness of the proposed algorithm are proved.
In order to filter out the interference of WIMAX (3.3～3.8 GHz) and WLAN (5.125～5.825 GHz) narrowband signals to Ultra WideBand(UWB) system, a Co-Planar Waveguide (CPW) fed miniaturized tapered slot antenna with dual band-notched characteristics is proposed. The CPW structure can effectively extend the bandwidth of the antenna and realize the full coverage of the whole UWB (3.1～10.6 GHz) frequency band. The dual band-notched characteristics (3.15～3.97 GHz and 4.94～6.05 GHz) are effectively achieved by etching the L-shaped slot in the antenna feeder and opening a pair of E-shaped slots in the radiating patch, which can inhibit WIMAX and WLAN interference to the UWB system. The antenna is simple and compact, and the size is very small, only 40 mm×18 mm×0.813 mm. The simulated and measured results show that the antenna has good notch and gain characteristics in the ultra wideband band, and can be used in miniaturized UWB system. The method has certain reference significance for the research of notched tapered slot antenna.
The kernel Extreme Learning Machine (ELM) has a problem that the kernel parameter of the Gauss kernel function is hard to be optimized. As a result, training speed and classification accuracy of kernel ELM are negatively affected. To deal with that problem, a novel kernel ELM based on K interpolation simplex method is proposed. The training process of kernel ELM is considered as an unconstrained optimal problem. Then, the Nelder-Mead Simplex Method (NMSM) is used as an optimal method to search the optimized kernel parameter, which improves the classification accuracy of kernel ELM. Furthermore, the K interpolation method is used to provide appropriate initial values for the Nelder-Mead simplex to reduce the number of iterations, and as a result, the training speed of ELM is improved. Comparative results on UCI dataset demonstrate that the novel ELM algorithm has better training speed and higher classification accuracy.
Conventional phased array radar transmits coherent signal to form antenna pattern. By transmitting and receiving antenna reuse its aperture is always less than MIMO radar. This paper firstly analyses the same points and the difference between MIMO radar and phased array radar. It further points out that the essential advantage of MIMO radar is the digital transmitting beam forming. Second it designs a diversity phased array radar in collision avoidance atmosphere. Its transmitting part uses phase array system, while the receiving part uses the Digital BeamForming (DBF). Through the analysis for the limitation of the digits phase shifter, it proves that this radar can achieve the same virtual aperture performance as MIMO radar, while the rader can avoid to produce the orthogonal signal. Finally through the computer simulation it verifies the feasibility and effectiveness of the method. This radar system can effectively reduce the cost and improve channel consistency under the premise of promised beam width.
Fluorescein Fundus Angiography (FFA) is regarded as the golden diagnostic criteria for fundus diseases. However, dislocation or rotation of the interested images on anatomic landmark (like retinal vascular branches, neovascularization), caused by inevitable eyeball movement, brings about difficulties in subsequent quantitative analysis and progress assessment of the diseases. In order to solve the above problems, a novel method based on mutual information is proposed for automatic registration of FFA image sequence. Firstly, the vessels of image sequence are segmented by multi-scale linear filter and down sampled hereafter by image pyramid. Then, the similarity of sampled images is calculated by mutual information and the evolution strategy is adopted to optimize the registration parameters. Finally, the transformation matrix with maximum mutual information is obtained to register the FFA image. Tests with FFA image sequences of 4 patients (total 1039 frames) show that the overall registration rate of the algorithm reaches 93% and the failure rate is only 1%. Compared with the classical registration methods, the proposed method shows better comprehensive performance in terms of registration rate, computing speed as well as robustness. It lays basic foundations for quantitative analysis on FFA images and potential clinical application.
Polar codes have outstanding error correction performance, but the code length of conventional polar codes is not compatible because of their coding method. To construct rate-compatible polar codes, a segmented puncturing method is proposed. Using the rate of polarization, the puncturing effect is measured and the codeword is removed to make the largest rate of polarization, which is the optimal puncturing mode. As the first codeword of the optimal puncturing mode is 0, the parity check codes are introduced to detect the decoding error of preceding segments codeword. The decoding performance of the method is simulated, results show that this method can obtain about 0.7 dB coding gain at 10–3 bit error rate compared with the traditional puncturing method, which can effectively improve the performance of the punctured polar codes.
A new algorithm is proposed to estimate jointly the four parameters (EDOD, ADOD, EDOA, ADOA) of targets based on the double-extension of aperture degree of freedom by combining MIMO technique with Minimum redundancy array under the conditions of non-uniform rectangular array configurations both on transmitter and receiver. Firstly, the two permutation matrices are constructed by utilizing the operational properties of the Khatri-Rao product of multiple matrices. Then the new data which possesses the characteristics of the double-extension of degree of freedom is attained by performing the twice row permutating, the redundant items deleting, multi-dimensional smoothing and data folding operations on the received data. The results illustrate that: the proposed method can estimate the four parameters of targets efficiently, and the estimated parameters are paired automatically without extra pairing operation. The parameter estimation performance of the proposed method is better than those of the ALS and multi-dimension ESPRIT methods under the same simulation conditions. And the proposed method can provide much stronger parameter identifiability than the conventional ones.
Because of nowadays airborne network’s updating difficulty of pre-allocated symmetrical key, high communication cost of public key certificate and the requirement of security channel for distributed identity-based key management, identity-based dynamic key management of airborne network is proposed. It is composed of two algorithms: self-organized generation of master key without the trusted third party and distributed management of user’s private key. Moreover, the master key share and user private partition can be delivered without the pre-established security channel by blinding them so that the scheme is easy to develop and flexible to extend. Finally, the correctness and security of the proposed scheme are proved, it is shown that it can provide the ability to resist the impersonation attack, replay attack and man-in-the-middle attack.
An equivalent circuit method of performance estimation for hexagonal loop composite absorbing metamaterial is proposed, and the corresponding equivalent circuit model is established. Based on the Fourier analysis of hexagonal lattice distribution, the equivalent distribution periodic parameter is proposed and the estimation method of RLC parameters is given according to the size of units. The applicability and accuracy of the equivalent circuit model for several structure dimensions are verified and compared with HFSS. A sample is fabricated and measured for further verification, which has a good broadband radar absorbing performance in 1.7～5.7 GHz.
To meet the diversified demand of 5G Network Slicing (NS), while ensuring the reliability of slice, to achieve the optimal allocation of network resources, considering the dynamic mapping and lightweight reliable mapping problem of network slicing, this paper proposes a joint allocation scheme of computing resources, link resources and the spectrum resources of Radio Remote Unit (RRU). Firstly, a multi-objective resource allocation model oriented to reliability constraints is established, and the Lyapunov optimization model is introduced to ensure the queue stability and optimize the resource allocation. Then, the virtual node mapping algorithm based on queue stability and virtual link mapping algorithm based on reliability are proposed. Finally, the time is discretized into a series of continuous time windows, and the online network slice mapping algorithm is implemented by using the time window dynamic processing of the incoming network slice request. Simulation results show that the proposed algorithm improves resource utilization and guarantees network reliability.
For personalized search based on location service, the trusted third-party server and peer node are used as the main method for privacy preserving. However, entirely trusted third-party server or peer node does not exist in real life. In order to address this problem, a method of privacy preserving on the location of mobile users is proposed when using personalized search. The method is used to convert the user’s location information into distance information and generate the user model according to the user’s query type, forming a query matrix with user location information, then the matrix is used to encrypt the user’s query and conceal the user information in the query matrix. Finally, according to the calculation of the security inner product, the K file with the highest relevance score is returned to the user. It is evident from the security analysis that the proposed method can effectively protect the user’s query privacy and location privacy. The analysis and experimental results show that the proposed method can greatly shorten the time of index construction and reduce the communication overhead. While providing users with location based personalized search results, the method is able to remedy the defects of small-screen mobile devices.
Traditional meter-wave radar usually suffers from the problem of Beam Split (BS) and Radar Blind Area (RBA) in the situation of low-grazing angle. To alleviate this difficulty, a Frequency Diverse Subaperturing Multiple-Input Multiple-Output (FDS-MIMO) radar is proposed and a theoretical framework for the analytical investigation of multipath characteristics is presented. The specular and perturbational multipath model is built, along with closed-form expression of the joint transmit-receive beampattern gain. Moreover, a notional concept of Multipath Mitigation Area (MMA) is defined together with the corresponding boundary conditions, and the Low Observability Rate (LOR) is defined as a performance benchmarkan to evaluate the FDS-MIMO radar beam overage capability. Next, the FDS-MIMO radar low-altitude beam coverage performance is optimized according to the soutions of the boundary conditions. Both theoretical analysis and numerical results demonstrate the advantages of FDS-MIMO radar over the conventional phased-MIMO radar in terms of low altitude beam coverage performance, and the BS and RBA of meter-wave radar is decreased by ultilizing the range-dependent beampattern.
Cross-axis coupling interference influences greatly the measurement accuracy of Three-Dimensional (3D) Electric Field Sensor (EFS). A MEMS-based One-Dimensional (1D) Electric Field Microsensor (EFM) chip with low coupling interference is presented, and a MEMS-based 3D EFS with low cross-axis coupling interference is developed by arranging three 1D EFM chips orthogonally. Different from previously reported 1D EFM chips sensitive to perpendicular electric field component, the proposed 1D EFM chip is designed to be symmetrical and connected to difference circuit, so that it is capable of sensing parallel electric field component perpendicular to axis of symmetry and eliminating coupling interference. The proposed 3D EFS has the advantages of small size and high integration. Experimental results reveal that in the range of 0～120 kV/m, the cross-axis sensitivities are within 3.48%, and the total measurement errors of this 3D EFS are within 7.13%.
Based on the characteristics of short pulse non-coherent radar, the parameterized signal model is established. By analysis on the reasons of no-coherence, compensative coherent processing algorithm based on matching filter and parameter estimation is proposed. The rationality of the compensative coherent processing is proved by the mathematical derivation as to single point target. Then, requirement for the range extended target is analyzed in theory, in the condition of approximate compensative coherent processing. Finally, the theoretical analysis results are verified by simulation.
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.
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.
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.
The diving SAR usually adopts the highly squint mode and sub-aperture to satisfy maneuvering and the 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.
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.
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.
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.
A wideband patch antenna array with low Radar Cross Section (RCS) based on metasurface is proposed. The 2×4 antenna array is composed of two kinds of slotted patch antennas working at different frequency band, realizing the miniaturization and bandwidth broadening of the antenna array. Then, based on the phase cancellation principle, low RCS performance is realized owing to the metasurface consisting of two Artificial Magnetic Conductor (AMC) structures in chessboard configuration. Simulated and measured results show that the working frequency band is expanded from 5.7~6.2 GHz to 5.6~6.6 GHz and radiation performance remains well with metasurface added to. Meanwhile the antenna monostatic RCS is reduced significantly. 3 dB RCS reduction is achieved over the range of 5.3 GHz to 7.0 GHz and the peak reduction is up to 31 dB under X polarization. While the 3 dB RCS reduction range is 5.8~6.9 GHz under Y polarization.
The vertical positioning accuracy of BeiDou satellite navigation System (BDS) and the continuity of receiver in the challenge environment can not satisfy the user demand. If atomic clocks are used in the receiver, the high stability of the atomic clock can be used for long time and high precision prediction of receiver clock bias. The positioning accuracy and continuity are improved by using atomic clock and barometric altimeter. This article first analyzes the atomic clocks and barometric altimeter aided BDS positioning algorithm; Then, correction method is proposed for initialization of barometric altimeter, and analysis on the difference of noise type clock is used to determine the clock bias prediction method; Finally, positioning experiment of the atomic clock and barometric altimeter aided BDS in simulation challenge environment is carried out, and the positioning result is analyzed. The results show that BDS can positioning solution to track two visible satellites, and vertical positioning accuracy is significantly improved. The positioning error in the vertical direction is decreased from 8.2 m (RMSE) to 5.2 m, and the fluctuation of the positioning results decreased from 4.6 m to 0.8 m.
To deal with the problem that most of the existing ship speed estimation algorithms can only estimate the slant range speeds of ships, a ship azimuthal speed estimation method based on local region Doppler centroid for Synthetic Aperture Radar (SAR) images is proposed. Firstly, the variation of Doppler centroid of moving target in local region of SAR image is analyzed and the theoretical formula for estimating the azimuthal speed using the slope of Doppler centroid variation is derived. Then, based on the probability density function of azimuthal power spectrum, an estimation method for the slope of Doppler centroid variation using the maximum likelihood estimation algorithm is presented. Moreover, the estimation accuracy and the applicability of the proposed method are also analyzed. Finally, the proposed method is implemented on simulated and filed data and the estimation results are compared with those obtained by directly calculating the frequency modulation rate. The results show that the proposed method has high estimation accuracy, which verifies the effectiveness of the proposed method.
Interrupted Sampling Repeater Jamming (ISRJ) is an advanced intensive false-target jamming with the advantages of fast interference response, anti-agile ability and so on. The radar signal is intermittently sampled with low-rate based on the principle of the under-sampling method, so that the radar can not detect the real targets and the jamming may overload the signal processing system. This article mainly focuses on the Interrupted Sampling Repeater Jamming. A sensitive Doppler sparse waveform is designed based on the ambiguity function theory to suppress the interference, which destroys the continuity of the interference signal output on different Doppler and suppresses the output of the intensive interference. Based on the analysis of the equivalent interval sidelobe, a method of sliding window extraction detection is proposed to achieve effective target detection while anti-jamming. Theoretical analysis and simulation experiments demonstrate the effectiveness of the interference suppression and the target detection performance in the interference.
Multiple sources localization is an issue of theoretical importance and practical significance in signal processing. The basis mismatch problem caused by target deviation from the initial grid point is addressed. Based on sparse Bayesian learning framework with Laplace prior, a novel iterative Adaptive Grid Multiple Targets Localization (AGMTL) algorithm is proposed to tackle the practical situation in which the targets deviates from the initial grid point. In essence, AGMTL algorithm implements sparse signal reconstruction and adaptive grid localization dictionary learning jointly. The simulation results show that AGMTL algorithm outperforms the traditional Compressive Sensing (CS) based localization algorithm in the terms of localization error, estimation reliability and noise robustness.
Independent Vector Analysis (IVA) is one of the best methods to solve the sort ambiguity of convolutive blind separation in frequency domain. However, it needs more iterations and computing time, and the separation effect is susceptible to the initial value of the separation matrix. This paper proposes an IVA convolutive blind separation algorithm based on step-size adaptive, which uses Joint Approximative Diagonalization of Eigenmatrices (JADE) algorithm to initialize the separation matrix and optimizes adaptively the step step-size parameters. JADE initialization can make the separation matrix have an appropriate initial value, thus avoiding the situation of local convergence; step-size adaptive optimization can significantly improve the convergence speed of the algorithm. Simulation results show that this algorithm improves the separation performance and shortens the operation time significantly.
Deep learning has a strong ability in the high-dimensional feature vector information extraction and classification. But the training time of deep learning is so long that the optimal hyper-parameters combination can not be found in a short time. To solve these problems, a method of product of experts system based on Restricted Boltzmann Machine (RBM) is proposed. The product of experts theory is combined with the RBM algorithm and the parameter updating way is all adopted the probability value, which leads to the undesirable recognition effect and slightly worse density models, so the parameter updating way is improved. An improved algorithm with momentum terms in different combinations is used not only in the RBM pre-training phase but also in the fine-tuning stage for both classification accuracy enhancement and training time decreasing. Through the recognition experiments on the MNIST database and CMU-PIE face database, the proposed algorithm reduces the training time, and improves the efficiency of hyper-parameters optimization, and then the deep belief network can achieve better classification performance. The result shows that the improved algorithm can improve both accuracy and computation efficiency in dealing with high-dimensional and large amounts of data, the new method is effective.
When using interactive genetic algorithm to solve big data information retrieval problem, single user needs to complete more human-machine interactive operation to achieve preference information extraction and optimization, thus it is easy to generate the problem of user fatigue and algorithm low efficiency. A multi-user strategy is introduced by making full use of the advantages of group decision to improve the sample utilization efficiency. First of all, multi-user collaborative type is devided into common collaboration or personalized collaboration according to the optimization goal which calculats user similarity and individual similarity based on user’s browsing behaviors. Then, individuals’ interval fitness is forecasted by sharing similar individual of similarity users. Based on phenotype similarity clustering, the large scale population individuals of " interval-interval” fitness assignment strategy is introduced. Finally, the best evaluation individual is recommended according to the similarities between offspring individuals and parent individuals. The proposed method is applied to decorative wallpaper design problem and is compared with existing typical methods. The experimental results confirm that the proposed algorithm has advantages in improving optimization quality and alleviating user fatigue while improving its efficiency in exploration.
For the issue of joint parameter estimation and symbol detection for multi-antenna signals with channel parameters difference over flat-fading channels, a new joint processing scheme is proposed based on the Variational Bayes (VB) method. The proposed scheme uses directly multiple received signals for the estimation of information symbols, restraining the information loss in conventional decoupled scheme of signals combination and demodulation. The problem is modeled as the joint Maximum A Posteriori (MAP) estimation of information symbols, time-delays, complex channel gains, and noise powers, given multiple observations, and approximately solved by means of VB approach. Based on the criterion of minimum relative entropy, analytical-form of the approximate distributions, i.e., variational distributions, for all unknown parameters are derived. There is no need to determine accurate point estimates of the parameters. Instead, the proposed scheme proceeds iteratively by alternating between the variational distributions of channel parameters and the information symbols. Simulation results show that the proposed joint processing scheme has significant performance improvements in comparison with conventional decoupled or partly joint processing schemes especially with large array sizes and short signal lengths.
In the process of target tracking, the sensor scheduling algorithm can achieve the tradeoff between the tracking error and the energy consumption so as to extend the service life of the sensor network. The issue can be modeled as a Partially Observable Markov Decision Process (POMDP), which takes both short- and long- term losses of sensor scheduling into account and makes a better decision. A C-QMDP approximation algorithm suitable for continuous state space is proposed. The Markov Chain Monte Carlo (MCMC) method is used to derive the transfer function of belief state and calculate the instantaneous cost. The state discretization method is used to solve the approximation of future cost based on Markov Decision Process (MDP) iteration. Simulation results show that compared to the existing POMDP approximation algorithms, the proposed algorithm can reduce the cumulative losses and computation load in the tracking process by offline computation.
Monthly Journal Founded in 1979
The Source Journal of EI Compendex The Source Journal of ESCI Database
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ISSN 1009-5896 CN 11-4494/TN
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