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Deconvolution-based Target Echo High-resolution Time Delay Estimation Technique Using Active Sonar
Siyuan CANG, Xueli SHENG, Hang DONG, Longxiang GUO
 doi: 10.11999/JEIT200649
[Abstract](47) [FullText HTML](19) [PDF 1841KB](15)
Abstract:
In view of enhancing the time delay estimation resolution for the target echo in a complex shallow-water environment, thus improving the target detection ability of the active sonar system. A high-resolution time delay estimation technique is proposed to detect the underwater target based on sparse representation theory and deconvolution framework. Firstly, the Toeplitz operator is introduced here to construct a dictionary matrix using the various time delayed replicas of the transmitting signal. Then, the estimated time-delay value can be found in the desired sparse vector solution. Secondly, the Alternating Direction Method of Multipliers (ADMM) is implemented to calculate the optimal solution globally. Thirdly, the reweighted iteration approach is explored to control the regularization parameter, thus suppressing the impact of the multipath channel. The arrival time of the echo can be decoupled to obtain a high-resolution time delay result. The simulated and experimental data verify that the proposed deconvolution-based time delay estimation technique can be used to detect the underwater target in shallow-water acoustic multipath channels. The resolution of the estimated time-delay result can achieve 0.056 ms.
Low-complexity Iterative Sparse Channel Estimation for Underwater Acoustic OFDM Systems Based on Generalized Path Identification Algorithm
Shiduo ZHAO, Shefeng YAN
 doi: 10.11999/JEIT200582
[Abstract](63) [FullText HTML](35) [PDF 1440KB](17)
Abstract:
In mobile OFDM underwater acoustic communication systems, the compressed sensing-based sparse channel estimation methods suffer from high computational complexity, which is not suitable for real-time communication. To solve this problem, this paper proposes a Generalized Path Identification (GPI) algorithm for estimating uniform Doppler distorted channel. This scheme first constructs equivalent transmitted symbols using Doppler spread matrices, and thus the channel is converted into an equivalent linear time-invariant one. Then the GPI algorithm is utilized to estimate the channel parameters. Furthermore, the GPI algorithm is extended to Turbo receivers to iteratively improve the channel estimation accuracy. Simulation results show that the performance of the proposed method is better than that of the conventional path identification algorithm, and is close to the Orthogonal Matching Pursuit (OMP) algorithm. Its computational complexity, however, is much lower than OMP algorithm.
A New Coprime Array with High Degree of Freedom Based on the Difference and Sum Co-array
Yupu CHEN, Xiaochuan MA, Xuan LI
 doi: 10.11999/JEIT200505
[Abstract](99) [FullText HTML](34) [PDF 1889KB](19)
Abstract:
To deal with the problem that the Degree Of Freedom(DOF) of uniform linear array is limited by the number of elements, a new type of coprime array is proposed called Displaced Coprime Array(DCA).It takes use of the conjugate augmented matrix which is formed by the time and space information of the received signal to obtain the equivalent difference and sum co-array and to estimate the Direction Of Arrival(DOA). DCA places the generalized coprime array at a certain distance from the single array element at the coordinate origin so that the elements of the sum co-array and the difference co-array are complemented. As a result, the use of DOF provided by the sum co-array can be maximized. In this paper, the closed-form expressions of the element positions and the placement distance of DCA are given. Then, the performance of the sum co-array and the difference co-array including the continuous elements and the hole positions is theoretically analyzed, the relationship between the two is given and high DOF of DCA is presented. Multiple simulations verify the effectivity of DOA estimation using DCA.
Estimation and Equalization of Time-varying Underwater Acoustic Channel Based on Superimposed Training and Low-complexity Turbo Equalization in Frequency Domain
Guang YANG, Hanxue DING, Qinghua GUO, Qi YAN, Xinjie WANG
 doi: 10.11999/JEIT200315
[Abstract](70) [FullText HTML](31) [PDF 3768KB](27)
Abstract:
To solve the problems of time-varying underwater acoustic channel estimation and equalization, an estimation and equalization algorithm of time-varying underwater acoustic channel based on Superimposed Training (ST) and Low-complexity Turbo Equalization (LTE) in frequency domain (ST-LTE) is proposed. Based on the ST scheme, the training sequence and symbols are linearly superimposed to make the channel information of the training sequence and symbols consistent; based on the least square algorithm, channel estimation is performed. Based on the interference elimination technique of training sequence in frequency domain, the interference of training sequence on symbols is eliminated in frequency domain; based on the Linear Minimum Mean Square Error (LMMSE) equalization algorithm in frequency domain, the low-complexity channel equalization (symbol estimation) is realized by the calculation of prior, posterior, extrinsic mean and variance; based on the Turbo equalization algorithm, soft reconstruction of superimposed training and update of channel estimation are conducted, the information exchange between equalizer and decoder is also carried out and the performance of channel equalization is extremely improved by using coding redundancy information. Simulation, static communication experiment in a pool (communication frequency is 12 kHz, bandwidth 6 kHz, the sampling frequency 96 kHz, the transmission rate of symbols 4.8 ksym/s and the power ratio of the training sequence on symbols 0.25:1) and moving communication experiment in Jiaozhou Bay (communication frequency is 12 kHz, bandwidth 6 kHz, the sampling frequency 96 kHz, the transmission rate of symbols 3 ksym/s and the power ratio of the training sequence on symbols 0.25:1) were carried out and simulation and experimental results verified the effectiveness of the proposed algorithm.
Modified Bayesian Group Target Track Initiation Algorithm Based on Algebraic Graph Theory
Qi JIANG, Rui WANG, Chao ZHOU, Tianran ZHANG, Cheng HU
 doi: 10.11999/JEIT200449
[Abstract](84) [FullText HTML](26) [PDF 3386KB](18)
Abstract:
Group target tracking is an efficient method to measure the states of airborne flocks. The first step of group target tracking is track initiation, including target clustering and track promotion. The state-of-the-art algorithms require mutual similarity between targets for clustering procedure, and track may be wrongly rejected due to the large residual of equivalent measurement. A modified Bayesian group track initiation algorithm based on algebraic graph theory is proposed. The clustering of measurement sets in surveillance volume is achieved by introducing the algebraic graph theory. The rejection of true track is avoided by modify the definition of classical Bayesian likelihood ratio. Results from actual field tests demonstrate the capability of clustering group targets precisely and promoting group tracks effectively.
A Low-Dose CT Image Denoising Method Based on Generative Adversarial Networkand Noise Level Estimation
Xiong ZHANG, Linlin YANG, Hong SHANGGUAN, Zefang HAN, Xinglong HAN, ANhong WANG, Xueying CUI
 doi: 10.11999/JEIT200591
[Abstract](17) [FullText HTML](2) [PDF 5898KB](7)
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Generative Adversarial Network (GAN) for Low-Dose CT (LDCT) image noise reduction has certain performance advantages, and has become a new research hot field of CT image noise reduction in recent years. When the intensity of noise and artifact distribution changes in LDCT images of different doses, the noise reduction performance of GAN network is unstable, and the generalization ability of the network is low. In order to overcome these shortcomings, this paper first designs a noise level estimation subnet with a code-decode structure to generate the noise maps corresponding to LDCT images with different doses, which is subtracted from the original input image to suppress initially the noise; Secondly, the backbone of the noise reduction network is designed as a multi-coded U-Net structure that is optimized through game confrontation to suppress further CT image noise; Finally, a variety of loss functions are designed to constrain the parameter optimization of each function modules, thus to guarantee further the performance of the LDCT image noise reduction network. Experimental results show that compared with current popular algorithms, the noise reduction network proposed in this paper can achieve a better noise reduction on the basis of retaining the original important information of LDCT images.
A Signal Modulation Method of Direct Sequence Spread Spectrum Based on Modulated m-sequence
Lingchuan ZENG, Yan BAI, Xiaochun LU, Hong YUAN, Chengeng SU, Xiaojing WU
 doi: 10.11999/JEIT200600
[Abstract](34) [FullText HTML](7) [PDF 2282KB](6)
Abstract:
To raise the spectrum utilization, a modulation and demodulation method called modulated Direct Sequence Spread Spectrum (m-DSSS) is proposed, which assign the period of m-sequence to be equal to the integer multiple of data bit. Firstly, the feasibility of m-DSSS signal acquisition is verified by numerical simulation results of correlation. Then, according to the permutation of information bits, a multi-channel signal modulation and demodulation method for m-DSSS is designed, and the mathematical model reacting the anti-interference ability of passing through the additive Gaussian white noise channel is established. Finally, the simulation and contrast test are carried out with Codes Shift Keying (CSK) modulation under the same spectrum utilization. The simulation results show that m-DSSS signal not only has lower side peak than CSK signal but also can assist signal acquisition by judging polarity when BPSK modulation is adopted. When bit error rate is better than 1e-3, m-DSSS signal has more than 2 dB advantage than CSK signal, which verifies the feasibility of m-DSSS used in DSSS system.
A Multi-domain Text Classification Method Based on Recurrent Convolution Multi-task Learning
Jinbao XIE, Jiahui LI, Shouqiang KANG, Qingyan WANG, Yujing WANG
 doi: 10.11999/JEIT200869
[Abstract](26) [FullText HTML](7) [PDF 103KB](2)
Abstract:
In the text classification task, many texts in different domains are similarly expressed and have the characteristics of correlation, which can solve the problem of insufficient training data with labels. The text of different fields can be combined with the multi-task learning method, and the training accuracy and speed of the model can be improved.A recurrent convolution multi-task learning model for text multi-classification is proposed, jointly modeling the text of multiple tasks, and taking advantage of multi-task learning, Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN) models to obtain the correlation between multi-domain texts, long-term dependence of text, and extract local features of text. Rich experiments are carried out based on multi-domain text classification datasets, the Recurrent Convolution Multi-Task Learning(MTL-LC) proposed in this paper has an average accuracy of 90.1% for text classification in different fields, which is 6.5% higher than the single-task learning model STL-LC. Compared with mainstream multi-tasking learning models Full Shared Multi-Task Learning(FS-MTL), Adversarial Multi-Task Learninng(ASP-MTL), and Indirect Communciation for Multi-Task Learning(IC-MTL) have increased by 5.4%, 4%, and 2.8%, respectively.
High-performance Hardware Architecture Design and Implementation of Ed25519 Algorithm
Bin YU, Hai HUANG, Zhiwei LIU, Shilei ZHAO, Ning NA
 doi: 10.11999/JEIT200876
[Abstract](21) [FullText HTML](10) [PDF 1400KB](4)
Abstract:
The speed of existing signature and verification architecture is difficult to meet the requirement of the specific applications domain, to solve this problem a high-performance hardware architecture of Ed25519 algorithm is developed. The scalar multiplication algorithm is implemented by using the window method with two-bits width to reduce the total cycle numbers of the algorithm significantly. By optimizing the order of operations of point addition and point doubling, the hardware utilization rate of multiplier is improved. The module multiplication is realized by using fast module reduction with low computational complexity, thus the overall operation speed is improved. The modular L algorithm based on Barrett reduction is proposed to reuse the fast modular reduction in scalar multiplications. By optimizing the modular power computation in the decompression process, the steps are simplified and the modular multiplication can be reused. Under the TSMC 55 nm CMOS process, the area of the proposed hardware architecture is 746 k equivalent gate, and the maximum frequency is up to 360 MHz. It can perform 90.6 k key generations, 88.2 k signatures and 39.9 k verifications per second.
Kernel Extreme Learning Machine Based on Alternating Direction Multiplier Method of Binary Splitting Operator
Yidan SU, Jia XU, Hua QIN
 doi: 10.11999/JEIT200884
[Abstract](29) [FullText HTML](7) [PDF 1860KB](1)
Abstract:
The Kernel Extreme Learning Machine (KELM) with convex optimization form has higher classification accuracy, but it takes longer time to train kelm with iterative method than solving linear equation method of traditional kelm. To solve this problem, an Alternating Direction Multiplier Method(ADMM) of Binary Splitting Operator is proposed to improve the training speed of convex optimization kernel extreme learning machine. Firstly, the process of finding the optimal solution of the kernel extreme learning machine is split into two intermediate operators by introducing a binary splitting operator, and then the optimal solution of the original problem is obtained through the iterative calculation of the intermediate operators. On 22 UCI datasets, the training time of the proposed algorithm is 29 times faster than that of the effective set method and 4 times faster than that of the interior point method. The classification accuracy of the proposed algorithm is also better than that of the traditional kernel extreme learning machine. On large-scale datasets, the training time of the proposed algorithm is better than that of the traditional kernel extreme learning machine.
Method on Alzheimer’s Disease Classification Utilizing Fuzzy Logic Feature Selection and Heterogeneous Ensemble Learning
Liang HAN, Ting YANG, Xiujuan PU, Qian HUANG
 doi: 10.11999/JEIT200963
[Abstract](23) [FullText HTML](7) [PDF 3329KB](6)
Abstract:
Early diagnosis of dementia is critical for timely treatment and intervention. Alzheimer’s Disease(AD) classification is an effective method on identifying AD at its early stage. In this paper, a feature selection method using improved Gauss fuzzy logic is proposed. Firstly, the normalized feature importance scores are calculated utilizing mutual information and variance analysis respectively. Then the final feature importance score is obtained by using improved Gauss fuzzy logic. At last, the features for AD classification are selected in accordance with the feature importance score. Furthermore, the heterogeneous ensemble classifier is constructed to classify AD patient utilizing selected features, which using logistic regression, random forest, LightGBM, support vector machine and depth feedforward network as primary classifier and multinomial naive Bayes classifier as secondary classifier. The proposed AD classification method is evaluated on the TADPOLE dataset. The experimental results show that the proposed feature selection method is effective and the integrated classifier based on Bayesian fusion is better than other conventional classification model on AD classification using the proposed feature selection method.
Azimuth Multichannel Reconstruction for Moving Targets in Spaceborne Squinted Multichannel Synthetic Aperture Radar
Wei XU, Zhengbin WEI, Pingping HUANG, Weixian TAN, Yaolong QI, Zhiqi GAO
 doi: 10.11999/JEIT200785
[Abstract](32) [FullText HTML](14) [PDF 10437KB](7)
Abstract:
In the spaceborne azimuth multichannel SAR squinted mode, the squint angle and the velocity of moving target will cause the 2-D spectrum of echo signal to be aliased and the multichannel imbalance, respectively. Both phenomena would affect the azimuth multichannel reconstruction for moving targets. To resolve this problem, an azimuth multichannel reconstruction method for moving targets in the azimuth multichannel squinted mode is proposed. It eliminates the secondary Doppler aliasing problem caused by the squint angle through azimuth de-ramp preprocessing, and then the multichannel imbalance caused by the moving target velocity is resolved by the improved multichannel reconstruction matrix. The clutter suppression ability in the case of channel redundancy is analyzed, and the residual phase error caused by the estimated velocity error is discussed. Furthermore, an effect moving target velocity estimate approach is proposed. Finally, the simulation results on point targets validate the proposed approach.
Time-Offset Generalized Frequency Division Multiplexing Communication in Doubly-selective Channels
Ying WANG, Shixiong YU, Jun REN, Bin LIN
 doi: 10.11999/JEIT200269
[Abstract](19) [FullText HTML](7) [PDF 1964KB](2)
Abstract:
The performance of Generalized Frequency Division Multiplexing (GFDM) systems significantly degrades over time-frequency doubly selective channels due to the severe inter-carrier interference and inter-subsymbol interference. To this end, a time-offset GFDM is proposed, which can improve the performance of GFDM systems under doubly selective channel environment by introducing a time-offset to the prototype filter of conventional GFDM systems. The average signal-to-interference ratio of GFDM systems in doubly selective channels is analytically derived, and a pilot-aided joint iterative channel estimation and symbol detection algorithm is proposed. The proposed algorithm can progressively decrease the interference signal and improve the performance of channel estimation and symbol detection with the information exchanging between the channel estimation unit and the successive interference cancellation based symbol detection unit. The results of theoretical analysis and simulation demonstrate that in doubly selective channels, the time-offset GFDM outperforms the conventional GFDM in terms of average signal-to-interference ratio and bit error rate, and the joint iterative channel estimation and symbol detection algorithm can reduce the bit error rate effectively.
Sea Clutter Data Augmentation Method Based on Deep Generative Adversarial Network
Bin DING, Xue XIA, Xuefeng LIANG
 doi: 10.11999/JEIT200447
[Abstract](17) [FullText HTML](6) [PDF 2343KB](4)
Abstract:
Due to the scarcity of sea clutter data, the high cost and long period of obtaining sea clutter data greatly limit the research of sea clutter characteristics and the application of ocean remote sensing. The method of sea clutter data generation based on the Generative Adversarial Networks (GAN) is studied. By extending the traditional GAN framework, a one-dimensional sea clutter data generation and identification model is formed. Based on the radar measured sea clutter data set, the generation and identification model training in the adversarial network is carried out. The amplitude distribution characteristics and time and spatial correlation of the sea clutter data generated by the model are analyzed. Based on the measured data, it is verified that the method can generate more sea clutter data with more variety, and similar distribution to the real sea clutter data.
Feature Selection Algorithm for Class Imbalanced Internet Traffic
Hong TANG, Dan LIU, LiShuang YAO, YunFeng WANG, ZuoFei PEI
 doi: 10.11999/JEIT190992
[Abstract](14) [FullText HTML](6) [PDF 4111KB](2)
Abstract:
Class imbalance always exists in the process of network traffic classification. Considering the problem, a new feature selection algorithm using weighted symmetric uncertainty and approximate Markov blanket is proposed. Firstly, a feature metric is defined using category distribution information, which is biased to minority classes. This makes it easier to pick out features which have strong correlation with minority classes. Then, considering the correlation between features and categories and between features and features, the weighted symmetry uncertainty and approximate Markov blanket were used to delete the unrelated features and redundant features. Finally, the feature dimension is further reduced to determine the optimal feature subset, by using feature evaluation functions based on correlation measures and sequence search algorithms. The experimental results demonstrate that the algorithm can effectively improve the classification performance of minority classes without sacrificing the accuracy of the overall classification.
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2021, 43(2): 0-0.  
[Abstract](34) [FullText HTML](12) [PDF 22913KB](15)
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contents
contents
2021, (2): 1-4.  
[Abstract](40) [PDF 252KB](10)
Abstract:
Special Topic on Non-stationary Non-Gauss Statistical Signal Processing
An Improved Time Delay Estimation Method Based on Generalized Cyclic Correntropy
Tianshuang QIU, Hao LIU, Jiacheng ZHANG, Jingchun LI, Rong LI
2021, 43(2): 255-262.   doi: 10.11999/JEIT200142
[Abstract](71) [FullText HTML](50) [PDF 2775KB](19)
Abstract:
In complex electromagnetic environment, due to the impact of co-channel interference and impulsive noise, the performance of existing time delay estimation algorithms degrade severely. In this paper, an improved Generalized Cyclic Correntropy time delay Estimation (HTGCCE) algorithm is proposed by using the Hyperbolic Tangent function to address this degradation problem. Firstly, the performance degradation of generalized cyclic correntropy method is thoroughly analyzed and explained in impulsive noise. Then, based on the hyperbolic tangent function, an improved generalized cyclic correntropy method is proposed to improve the delay estimation performance under impulsive noise. Finally, the simulation results show that the proposed time delay estimation algorithm has an outstanding performance in impulsive noise, even with small characteristic component and low generalized signal-to-noise ratio.
Convex Combination of Multiple Adaptive Filters under the Maximum Correntropy Criterion
Mingfei LU, Siyuan PENG, Badong CHEN
2021, 43(2): 263-269.   doi: 10.11999/JEIT200288
[Abstract](55) [FullText HTML](41) [PDF 1622KB](16)
Abstract:
The adaptive filtering algorithms under the Maximum Correntropy Criterion (MCC) show strong robustness against impulsive noises. The original MCC adaptive filter, however, still suffers from a compromise between convergence rate and misadjustment when choosing parameters. To address this issue, a convex combination approach is proposed in this paper, where multiple MCC adaptive filters with different step-sizes and kernel widths are combined together to yield fast convergence speed and lower misadjustment. Theoretical analysis on convergence of the new approach demonstrates that it can achieve more desirable performance than the original MCC adaptive filter as well as convex combination of two MCC adaptive filers with different step-sizes or kernel widths. Simulation results confirm the excellent performance of the new method.
High Resolution Angle Parameter Estimation for Bistatic EMVS-MIMO Radar Based on a New Designed Array
Qianpeng XIE, Xiaoyi PAN, Jiyuan CHEN, Shunping XIAO
2021, 43(2): 270-276.   doi: 10.11999/JEIT200130
[Abstract](91) [FullText HTML](25) [PDF 1163KB](15)
Abstract:
A new transmit and receive array in bistatic ElectroMagnetic Vector Sensor Multiple-Input Multiple-Output (EMVS-MIMO) radar system is designed to improve the angle parameter estimation accuracy. Compared with the bistatic EMVS-MIMO radar equipped with half -wavelength spaced uniform linear arrays both in the transmitter and receiver, the new designed transmit and receive array can further enhance the array aperture. And, automatically paired angle parameter matching process for 2D DOD and 2D DOA can be obtained with the aid of the parallel factor trilinear alternating least square algorithm. Meanwhile, the corresponding elevation angle, azimuth angle, polarization angle and polarization phase difference both for transmitter and receiver are also automatically paired. Then, the loading matrices corresponding to transmit and receive array can be obtained by using the parallel factor trilinear alternating least square algorithm. And, high-accuracy and low-accuracy direction sine estimation can be determined by extracting the rotation invariance relationship from the obtained loading matrices. Thus, high resolution angle parameter estimation can be located by combining the high-accuracy estimated results and low-accuracy estimated results. Furthermore, the proposed method can provide automatically paired angle parameter matching process and lower computation complexity than state-of-the-art methods. Simulation results are carried out to verify the excellent angle parameter estimation performance of the proposed method.
Parameter Estimation of LFM Signals Based on Compress Transform Function in Impulsive Noise
Yan JIN, Penghui CHEN, Hongbing JI
2021, 43(2): 277-283.   doi: 10.11999/JEIT200342
[Abstract](79) [FullText HTML](35) [PDF 2223KB](25)
Abstract:
In order to solve the problem that existing parameter estimation algorithms of Linear Frequency Modulation (LFM) signals undergo performance degradation or even become invalid in impulsive noise environment, a new method for estimating LFM signal parameters in impulsive noise is proposed in this paper. The paper constructs a new Compress Transform (CT) function, analyzes the approximate linearity of the function near the zero point, derives that the second-order moments are bounded after the proposed transformation for any random variable, and proves that the initial frequency and frequency modulation slope information of an LFM signal are unchanged after the transformation. According to the relationship between the peak coordinates and the signal parameters in the FrFT domain, the peak point in the transform domain is located and the signal parameters estimates can be obtained. Simulation results show that the proposed method can effectively suppress the impulse noise and accurately estimate the parameter information of the signal. This method is simple and robust. Moreover, it does not require the prior information of the impulsive noise.
A Logarithmic Total Least Squares Adaptive Filtering Algorithm for Impulsive Noise Suppression
Haiquan ZHAO, Lei LI
2021, 43(2): 284-288.   doi: 10.11999/JEIT200344
[Abstract](219) [FullText HTML](82) [PDF 1348KB](37)
Abstract:
In environments where both the input and output signals of the unknown system contain noise, classical adaptive filtering algorithms, such as the Least Mean Square (LMS) algorithm, will produce biased estimates. The Total Least Squares (TLS) method is devised to minimize the perturbation of errors in the input and output signals, which is an important method to solve such problems. However, when the signals are disturbed by impulsive noises, which exist in many practical applications, the performance of traditional adaptive filtering algorithms that only relies on the second-order statistics of the errors, including the TLS algorithm, will deteriorate seriously, so that it can not work properly. In order to solve this problem, based on the TLS method, this paper uses logarithmic function to improve the TLS algorithm, and proposes a Logarithmic Total Least Square (L-TLS) algorithm which can efficiently reduce the effects of impulsive noises. Finally, computer simulation experiments verify the effectiveness of the proposed algorithm.
A Multipath TDOA Estimation Algorithm Based on Correntropy under Impulsive Noise Environment
Sen LI, Jifu WANG, Bin LIN
2021, 43(2): 289-295.   doi: 10.11999/JEIT200358
[Abstract](106) [FullText HTML](40) [PDF 1722KB](12)
Abstract:
In order to realize the high-resolution multipath Time Difference Of Arrival (TDOA) estimation which is not limited by the resolution limit of correlation method under impulsive noise environment, a Correntropy Expectation-Maximum (CEM) high resolution multipath TDOA estimation algorithm is proposed based on the Maximum Correntropy Criterion (MCC). The multipath TDOA is estimated by transforming multi-dimensional optimization problems into multiple one-dimensional optimization problems. The simulation results show that the CEM algorithm has good estimation performance under strong impulsive noise and low SNR environment, and the selection of kernel size in CEM algorithm is not depend on the prior information of the impulsive noise.
Adaptive ASR Filtering in Impulsive Noise Environments
Yan JIN, Yagang LI, Hongbing JI
2021, 43(2): 296-302.   doi: 10.11999/JEIT190793
[Abstract](74) [FullText HTML](36) [PDF 3050KB](6)
Abstract:
In the field of impulsive noise processing based on alpha-stable distribution model, the classical filtering methods have been largely motivated by special cases of alpha-stable family such as Cauchy distribution and Meridian distribution, and their pulse suppression ability is limited. To address the above limitations, a class of robust cost functions are devised and a robust filtering method ASR (\begin{document}${\rm{AS}}\alpha {\rm{S}} $\end{document} kobust) is proposed, based on the M estimation theory and the \begin{document}$ {\rm{AS}}\alpha {\rm{S}} $\end{document} distribution model, with its robustness analyzed by the influence functions. Then the expression of the linearity parameter is proposed and a threshold selection method is adopted for an adaptive linearity parameter selection. The unified theoretical framework of robust filtering is devised, and Myriad filters and Meridian filters are interpreted within the unifying framework. In addition, a filtering method which is, namely AS-FT (ASR STFT) is developed and the parameters estimation of noisy Linear Frequency Modulation (LFM) signals shows the robustness of ASR filtering. Simulation results show that the ASR method is more robust to outliers than Myriad filters, Median filters, fractional lower-order statistics and other traditional robust filters.
Variable Step Size Sign Diffusion Affine Projection Algorithm Based on Wilcoxon Norm under Non-Gaussian Noise
Ying GUO, Hefang YU, Lu ZHAO, Fei LI, Zhenyu LIU
2021, 43(2): 303-309.   doi: 10.11999/JEIT200371
[Abstract](118) [FullText HTML](68) [PDF 1161KB](42)
Abstract:
Diffusion Affine Projection Algorithm (DAPA) is an important method to realize the adaptive estimation of distributed network parameters. The algorithm can converge rapidly even when the input signal has correlation. The disadvantage of DAPA is that the ability to suppress non-Gaussian noise with impulsive characteristics is weak, and the fixed step size limits the performance of the algorithm. In this paper, a Variable Step size Sign Diffusion Wilcoxon Affine Projection Algorithm (VSS-DWAPA) is proposed. Firstly, the Wilcoxon norm which has strong ability to resist outliers is introduced as the cost function, and sign quantization is carried out according to its value characteristics, and then a new iterative equation is derived. Secondly, considering the limitation of fixed step size, the control of error signal to step size is realized through iterative method. That is, in the initial stage and the almost convergent stage, the step size is selected differently, which effectively makes it have better adaptation. The simulation results show that the proposed VSS-DWAPA is superior to some existing diffusion adaptive filtering algorithms in convergence, stability and tracking. It can also work well in Gaussian noise environment.
Study on High Efficient Algorithm for Cyclic Correntropy Spectral Analysis
Hui LI
2021, 43(2): 310-318.   doi: 10.11999/JEIT200113
[Abstract](65) [FullText HTML](31) [PDF 2083KB](12)
Abstract:
A high computationally efficient algorithm for cyclic correntropy spectral analysis is presented which is named as Correntrogram algorithm. Correntrogram algorithm overcomes the problems of Cyclic Periodogram Detection (CPD) method, such as high computational cost, low resolution and spectrum leakage. Correntrogram utilizes the advantages of Wigner-Ville Distribution (WVD) which has high time frequency resolution. By replacing the time-varying autocorrelation function with the time-varying auto-correntropy function in the WVD algorithm, the cyclic auto-correntropy spectral density estimation algorithm can be realized. First, the time-varying auto-correntropy function matrix of the signal is calculated, and then the Fast Fourier Transform (FFT) of each row of the time-varying auto-correntropy function matrix is computed to get the cyclic auto-correntropy function matrix. Finally, the FFT of each column of the cyclic auto-correntropy function matrix is calculated to get the cyclic auto-correntropy spectral density function. The validity of the proposed estimator is demonstrated on a simulative amplitude modulation signal. The simulative result shows that not only the proposed estimator is computationally efficient, but also has high frequency resolution and overcomes the spectrum leakage. The performance of Correntrogram is better than that of CPD method.
Papers
Synchronous Frequency Hopping Signal Network Station Sorting Based on Underdetermined Blind Source Separation
Hongguang LI, Ying GUO, Dongwei ZHANG, Yinsong YANG, Zisen QI, Ping SUI
2021, 43(2): 319-328.   doi: 10.11999/JEIT190920
[Abstract](42) [FullText HTML](20) [PDF 2388KB](13)
Abstract:
Considering the problem of synchronous Frequency Hopping(FH) network station sorting, an Underdetermined Blind Source Separation(UBSS) algorithm based on time-frequency domain single source point detection is proposed. Firstly, the algorithm performs time-frequency transform on the observed signal, and uses adaptive threshold denoising algorithm to eliminate the background noise of the time-frequency matrix. It can increase the algorithm anti-noise performance. Then, single source point detection is performed according to the absolute azimuth difference of the signal. It can effectively ensure the sufficient sparsity of a single source point. The hybrid matrix estimation is completed by the improved fuzzy C value clustering algorithm. It can reduce the influence of noise and sample set distribution differences and improve the estimation accuracy. Finally, the source signal is reconstructed and restored by a variable step size Sparsity Adaptive Subspace Pursuit(SASP) algorithm. The simulation experiments show that the proposed algorithm has higher recovery accuracy of the frequency hopping signal under the condition of low Signal to Noise Ratio (SNR), and can effectively complete the blind separation of the synchronous frequency hopping signal.
Method for Detecting the Delay-forwarding ADS-B Messages and Positioning of Jammer
Zhigang SU, Jing GUAN, Jingtang HAO, Bing HAN, Jun ZHOU
2021, 43(2): 329-334.   doi: 10.11999/JEIT191006
[Abstract](60) [FullText HTML](33) [PDF 914KB](10)
Abstract:
Automatic Dependent Surveillance-Broadcast (ADS-B) technology is faces serious security risk of spoofing due to the characteristics of broadcasting the clear text. In view of the detection of delay-forwarding messages and the positioning of corresponding jammers, according to the distribution characteristics of the interval between radiation positions of messages received by ADS-B ground station, a method is proposed for detecting delay-forwarding messages by using the difference between the instantaneous velocity of sequential messages and the average velocity of interest of period. According to the spatial relationship between the positions of the original messages of those delay-forwarding messages and the jammer, the positions of the delay-forwarding messages received by ADS-B ground station and their received time stamps are used to locate the position of jammer with the multilateration technique. The simulation results show that the difference between the instantaneous velocity and the average velocity can effectively detect the presence or absence of delay-forwarding messages, the positioning accuracy of jammer is related to the timing accuracy of ADS-B ground station, the number of messages used for positioning, the distance between jammer and track and the azimuth of jammer. The better positioning performance of jammer can be obtained by using the message positions in side-looking area. The proposed method can help the ADS-B ground station independently to detect the delay forwarding interference and to locate the corresponding jammer.
A Kind of Versoria Function Normalized Adaptive Filtering Algorithm
Yuanlian HUO, Xiaoqiang LONG, Peijun LIAN, Danfeng WANG
2021, 43(2): 335-340.   doi: 10.11999/JEIT191003
[Abstract](68) [FullText HTML](31) [PDF 1843KB](10)
Abstract:
On the basis of the comprehensive consideration in the design of the indexs of the adaptive filter algorithm convergence speed, steady-state error, computational complexity and tracking performance, a kind of Versoria function normalized adaptive filtering algorithm is proposed in this paper. The class Versoria function is used instead of Sigmoid function as step iterative formula, introducing variable step size based on the relevant error adjustment principle, the stability of the algorithm is enhanced greatly. At the same time, the convergence speed and tracking performance of the algorithm is promoted and the computational complexity of the algorithm is reduced. The influence of the parameter \begin{document}$\alpha $\end{document}, \begin{document}$\beta $\end{document} and \begin{document}$\gamma $\end{document} different value of the step function of algorithm is analyzed on Matlab platform. Compared with the Sigmoid function variable step size LMS algorithm and variable step size LMS algorithm based on Versoria function, and the simulation results show that this algorithm has faster convergence speed, better tracking ability, smaller steady-state error and strong robustness.
Sensor Selection Method Based on Multi-objective Optimal Optimization for Mixture Gaussian Noise
Qingli YAN, Jianfeng CHEN
2021, 43(2): 341-348.   doi: 10.11999/JEIT191031
[Abstract](91) [FullText HTML](35) [PDF 1752KB](32)
Abstract:
To overcome the flaw that the sensor selection methods based on either of Bayesian Fisher information matrix or mutual information could not provide coincident results, the multiple objective optimal technology is developed for sensor selection by minimizing the number of sensors, maximizing corresponding Bayesian Fisher information matrix and mutual information of the selected sensors. Then, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach is proposed to find the candidate that can better trade off the cost and two performance metrics. Comparison results demonstrate that the proposed method can find a better sensor group, and ultimately, its overall localization performance is more stable and accurate.
Robust Nonnegative Least Mean Square Algorithm Based on Sigmoid Framework
Kuan’gang FAN, Haiyun QIU
2021, 43(2): 349-355.   doi: 10.11999/JEIT200018
[Abstract](244) [FullText HTML](78) [PDF 4036KB](28)
Abstract:
Impulsive noise causes nonnegative algorithms to yield excessive error during iterations, which will damage the stability of the algorithm and causes performance degradation. In the paper, a NonNegative Least Mean Square algorithm based on the Sigmoid framework (SNNLMS) is proposed. The algorithm embeds the conventional nonnegative cost function into the Sigmoid framework to receive a new cost function. The new cost function has the characteristics of suppressing the impact of impulse noise. In addition, in order to enhance the robustness of the SNNLMS algorithm under sparse system identification, the Inversely-Proportional Sigmoid NonNegative Least Mean Square (IP-SNNLMS) is proposed based on the inversely-proportional function. Simulation results demonstrate that the SNNLMS algorithm effectively solves the problem of misadjustment caused by impulsive noise. IP-SNNLMS enhances the robustness of the algorithm and improves the defect of the convergence rate of the SNNLMS algorithm under the sparse system identification.
Airborne Distributed Coherent Aperture Radar Synchronization Error Calibration Method Based on Prominent Points
Jinming CHEN, Tong WANG, Jianxin WU, Xiaoyu LIU
2021, 43(2): 356-363.   doi: 10.11999/JEIT190694
[Abstract](75) [FullText HTML](20) [PDF 2354KB](13)
Abstract:
Airborne Distributed Coherent Aperture Radar (DCAR) has the advantages of wide observation range, high maneuverability and flexible deployment. However, airborne DCAR is confronted with more stringent time, space and phase synchronization requirements. Therefore, an airborne DCAR signal model and its matrix representation based on Slow-Time Code Division Multiple Access (ST-CDMA) waveform are established successively. Moreover, the influence on target coherence synthesis resulted from time, space and phase synchronization errors is analyzed in detail, and a novel airborne DCAR synchronization error calibration method based on prominent points is proposed. This method utilizes the target parameter search strategy to eliminate the grid mismatch filtering firstly. Then, with the utilization of estimation approaches based on target model or repeater station model, the unit platform position error is calibrated. Finally, equivalent amplitude and phase errors are calibrated by Eigen structure methods. The validity of the proposed method to calibrate the airborne DCAR synchronization error is demonstrated by simulation experiments.
Maneuvering SAR Imaging Algorithms Based on the Separation of Azimuthal Motion Information
Yanheng MA, Jianqiang HOU, Gen LI, Xinhai LIU
2021, 43(2): 364-371.   doi: 10.11999/JEIT190757
[Abstract](44) [FullText HTML](20) [PDF 2844KB](5)
Abstract:
A maneuvering SAR imaging algorithm is proposed based on the separation of azimuthal motion information. After range compression, the influence of non-azimuthal motion on range walk and spatiality is removed by separating azimuthal motion information. Then, the azimuth velocity equivalent transformation is used to realize range bending correction. Finally, the azimuth compression is realized by non-uniform Fourier transform. Using this principle, the parameters of three directions in the oblique distance equation have the same properties in the three-dimensional coordinate system. The algorithm transforms the three-dimensional spatial motion into the azimuthal non-uniform motion model. Through simulation, the applicability of the algorithm to different models and other motion states is verified. The algorithm is simple, stable and applicable.
Soft Output Signal Detection for Massive MIMO Systems Based on Chebyshev Trace Iteration
Xiaorong JING, Jingjing WEN, Weijia LEI
2021, 43(2): 372-379.   doi: 10.11999/JEIT191048
[Abstract](30) [PDF 0KB](3)
Abstract:
Minimum Mean Square Error (MMSE) algorithm can achieve near-optimal detection performance for multi-user massive Multiple Input Multiple Output (MIMO) systems. However, the calculation of the high-dimensional matrix inversion required in MMSE algorithm causes excessively high computational complexity, which makes it difficult to implement quickly and effectively in practical applications. At the same time, for the Higher Quadrature Amplitude Modulation (HQAM), the direct use of hard decision to realize the symbol-to-bit demapper will result in an obvious performance loss. Therefore, a low-complexity soft output signal detection algorithm based on Chebyshev-Trace Iteration (CTI) is proposed for Gray-coded HQAM in this paper. The algorithm not only effectively avoids the calculation of the high dimensional matrix inversion, but also gives a simplified calculation method with the trident list searching to compute the bit Log-Likelihood Ratio (LLR) by using the bit flip property of Gray-coded modulation and binary tree architecture. The simulation results show that the proposed soft output detection algorithm needs at most 3 iterations to converge and achieve detection performance close to MMSE, which achieves a good tradeoff between the complexity and the detection performance.
Joint Virtual User Identification and Channel Security En/Decoding Method for Ad hoc Networks
Kenan ZHANG, Guofang TU, Can ZHANG, Deyuan CHEN
2021, 43(2): 380-387.   doi: 10.11999/JEIT190644
[Abstract](44) [FullText HTML](25) [PDF 2217KB](10)
Abstract:
Ad hoc network is a kind of self-organized network without center. The reliability of virtual user identification and channel security are reduced when SNR is low due to user energy limitation. In order to solve this problem, a joint virtual user identification and channel security en/decoding method is proposed in this paper. Transmitter-receiver-based virtual user identification code is generated by xoring the orthogonal address code of transmitter with the pseudo random address code of receiver and encrypted with channel security code as key to acquire orthogonal random secure sequence so as to improve channel security. In order to realize spread spectrum as well as improve transmission efficiency, transmitted data is divided into 6-bit symbols, each symbol is mapped with an orthogonal random secure sequence. Receivers adopt subspace-based method to process received data and establish a judgment model to identify virtual users. Simulation results indicate that the proposed method obtains 1.6 dB \begin{document}${{E_b}}/{{N}}_0$\end{document} gains compared with existing methods when miss alarm rate of virtual user identification is 10–3.
Software-defined Power Communication Network Routing Control Strategy Based on Graph Convolution Network
Min XIANG, Huayang RAO, Jinjin ZHANG, Mengxin CHEN
2021, 43(2): 388-395.   doi: 10.11999/JEIT190971
[Abstract](657) [FullText HTML](262) [PDF 2871KB](76)
Abstract:
Transmission delay and packet loss rate are critical issues in reliable transmission of power communication services. A minimum path selection routing control strategy for software-defined power communication networks is proposed. Combining the characteristics of the centralized control structure of the software-defined power communication network, a Link Bandwidth Occupancy Predictive model based on Graph Convolutional Network (LBOP-GCN) is built to analyze the route paths bandwidth occupancy in the next period. The selectivity (Q) of different transmission paths from the source node is calculated to the destination node is calculated by using Triangle Modular Operator (TMO) to fuse the transmission delay of the path, the path bandwidth occupancy at the current moment and the path bandwidth occupancy at the next moment. Then the path with the lowest Q value is used as the flow table of the OpenFlow switch delivered by the Software Defined Network (SDN) controller. Experiments show that the proposed routing control strategy can effectively reduce service transmission delay and packet loss rate.
The Optimization of Wireless Sensor Network Topology Based on FW-PSO Algorithm
Ying ZHANG, Guangyuan YANG
2021, 43(2): 396-403.   doi: 10.11999/JEIT191039
[Abstract](165) [FullText HTML](60) [PDF 1116KB](19)
Abstract:
Wireless Sensor Network (WSN) has the characteristics of scale-free network, usually works in an unattended open environment, and is vulnerable to a variety of deliberate attacks. The attack causes the network to break down, and even causes the whole network to be paralyzed. In this paper, the scale-free network in complex network is taken as the research object, and a scale-free wireless sensor network model is constructed. Using the advantages of Fireworks algorithm and Particle Swarm Optimization (PSO) algorithm, such as search ability and population diversity, the FW-PSO (FireWorks and Particle Swarm Optimization) algorithm is proposed, which has good performance in global search ability and convergence speed. For the scale-free network model, FW-PSO algorithm is used to optimize the network topology. Under different attack strategies, the performance of the network before and after the optimization is analyzed from dynamic and static invulnerability respectively. Simulation results show that, compared with other similar algorithms, the dynamic and static invulnerability of wireless sensor network optimized by the proposed algorithm has obvious advantages.
Virtual Network Function Migration Optimization Algorithm Based on Deep Deterministic Policy Gradient
Lun TANG, Lanqin HE, Qi TAN, Qianbin CHEN
2021, 43(2): 404-411.   doi: 10.11999/JEIT190921
[Abstract](88) [FullText HTML](21) [PDF 2046KB](28)
Abstract:
To solve the problem of Virtual Network Function (VNF) migration optimization, which is caused by the dynamic change of resource requirements of Service Function Chain (SFC) under Network Function Virtualization/ Software Defined Network (NFV/SDN) architecture, a VNF migration optimization algorithm is proposed based on deep reinforcement learning. Firstly, based on the underlying CPU, bandwidth resources and SFC end-to-end delay constraints, a Markov Decision Process (MDP) based stochastic optimization model is established. This model is used to optimize jointly network energy consumption and SFC end-to-end delay by migrating VNF. Secondly, since the state space and action space of this paper are continuous value sets, a VNF intelligent migration algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to obtain an approximate optimal VNF migration strategy. The simulation results show that the algorithm can achieve the compromise between network energy consumption and SFC end-to-end delay, and improve the resource utilization of the physical network.
EEG Feature Extraction Based on Brain Function Network and Sample Entropy
Zhizeng LUO, Xianju LU, Ying ZHOU
2021, 43(2): 412-418.   doi: 10.11999/JEIT191015
[Abstract](99) [FullText HTML](49) [PDF 2007KB](30)
Abstract:
For the low recognition rate of motor imagery ElectroEncephaloGram (EEG) signals using single feature in Brain-Computer Interface (BCI) research, a feature extraction method combining brain function network and sample entropy is proposed. According to the neural mechanism appearing in Event Related Synchronization/Event Related Desynchronization (ERS/ERD) phenomenon and the contralateral mapping mechanism between cortex and limb motor imagery, the μ rhythm is denoised by wavelet packet transform. The brain function network is constructed for left hemispherical brain region and right hemispherical brain region by μ rhythm of 27 left channels and 27 right channels respectively. The mean node degree and the mean clustering coefficient are calculated as the brain function network characteristics, and the feature vectors combining the distribution and directivity are constructed by the sample entropy of C3 and C4 channels with the μ rhythm. The Support Vector Machine (SVM) is used to classify the left hand and right hand motor imagery EEG signals. The results show that the feature extraction method based on brain function network and sample entropy achieves better classification result, and the highest classification rate reached 90.27%.
Construction of Convolution Compressed Sensing Measurement Matrices Based on Cyclotomic Classes
Yubo LI, Jingjing ZHANG, Chenghuan HAN, Xiuping PENG
2021, 43(2): 419-425.   doi: 10.11999/JEIT190878
[Abstract](54) [FullText HTML](27) [PDF 2139KB](13)
Abstract:
Convolutional compressed sensing emerging in recent years is a new type of compressed sensing technology. By using cyclic matrix as measurement matrices, the sampling in convolutional compressed sensing can be simplified into convolution process, thus the complexity of the algorithm is greatly reduced. In this paper, a construction of measurement matrices for convolutional compressed sensing based on cyclotomic classes is proposed. The measurements are obtained by using the circulate convolution signal of the deterministic sequence and then by random subsampling. The correlation of the measurement matrix constructed in this paper is smaller than that of the existing constructions in the literature. The simulation results show that the measurement matrix constructed in this paper can recover the sparse signal better than the random Gaussian matrix under the same conditions. The proposed matrix can also be applied to channel estimation and reconstruction of two-dimensional images.
Research Progress on Underwater Optical Image Processing
Yinjing GUO, Qi WU, Jiaojiao YUAN, Jiachen HOU, Wenhong LÜ
2021, 43(2): 426-435.   doi: 10.11999/JEIT190803
[Abstract](111) [FullText HTML](47) [PDF 3495KB](46)
Abstract:
Underwater optical image processing is an important basis for underwater equipment to complete deep-sea exploration and operation tasks. Based on a brief description of the research background, significance and hotspots of underwater optical image processing, this paper gives a detailed overview of underwater imaging technology and clearness of underwater images from the aspects of improving the lighting factors and color correction of underwater images. The research progress focuses on the research status of the two most active research directions of image restoration methods and image enhancement methods based on imaging models. According to the research hotspots of underwater optical image processing, the research of underwater optical image processing is prospected from the perspectives of considering the forward refraction of light, combining underwater imaging models and image enhancement algorithms, introducing new algorithms in related fields, and improving the real-time performance of image processing.
Design of a Multi-scroll Chaotic System and its Application to Image Encryption
Yulong BAI, Yang YANG, Lihong TANG
2021, 43(2): 436-444.   doi: 10.11999/JEIT191002
[Abstract](73) [FullText HTML](38) [PDF 1902KB](16)
Abstract:
A chaotic system with three scrolls is proposed. The dynamics characteristics of the system, such as Lyapunov index, bifurcation diagram, Poincare cross section diagram, power spectrum and equilibrium stability, are studied by numerical simulation. The analysis results show that the system has good dynamics characteristics and rich topological attractors. In addition, an experimental simulation circuit is developed based on the circuit simulation software Multisim, which has a simple structure and is easy to realize in practice. Moreover, the simulation experiment is very consistent with the theoretical analysis conclusion, confirming that the proposed chaotic system circuit can be realized physically, thus verifying the chaos generation capacity of the chaotic system. Finally, a color image encryption algorithm is designed based on DNA algorithm, and the results show that the system has high security performance.Using the new system chaotic sequence to encrypt image, the encryption histogram and correlation between adjacent pixels are analyzed, results show that the new systems are very sensitive to image key and plaintext, key space is large, chaotic system applied to image encryption has higher safety performance.
A Novel Noise Reduction Differential Chaos Shift Keying System Based on Quadrature Modulation
Gang ZHANG, Jinhui LIU, Tianqi ZHANG
2021, 43(2): 445-453.   doi: 10.11999/JEIT190955
[Abstract](86) [FullText HTML](21) [PDF 2486KB](11)
Abstract:
To overcome the shortcomings of low transmission rate of Noise Reduction Differential Chaos Shift Keying (NR-DCSK), a novel Noise Reduction Differential Chaos Shift Keying system based on Quadrature Modulation (QM-NRDCSK) is proposed. The generator generates two chaotic sequences, the reference signal of each channel is P-time repetition of the information-bearing signal. The information of different users is distinguished by different time slots, and the two signals are transmitted on the same frequency band by using quadrature modulation. The reference signal of each channel is averaged P times by the moving average filter at the receiving end, and then non-coherently demodulated with the information signal. The correctness of the theoretical derivation is verified by simulations in AWGN and multi-path Rayleigh fading channels, and it shows that the system can effectively improve the transmission rate and has better bit error performance while having high spectrum utilization.
Determination and Properties Analysis of a Cubic Polynomial Chaotic Map
Hongyan ZANG, Xinyuan WEI, Yue YUAN
2021, 43(2): 454-460.   doi: 10.11999/JEIT190875
[Abstract](56) [FullText HTML](39) [PDF 1977KB](7)
Abstract:
This paper provides the sufficient conditions for topological conjugation between the general cubic polynomial maps and a piecewise linear chaotic map, then provides indirectly the sufficient conditions that make the cubic polynomial maps be chaotic. This paper analyzes further the uniformity, structural complexity and randomness of the piecewise linear map and cubic polynomial maps of topological conjugation. The results show that the uniformity of the piecewise linear map is better than the polynomial maps while the randomness of the polynomial maps is superior to the piecewise linear map. As for the structural complexity, there is no significant difference between the two kinds of systems, but it should be noted that the quantitative method makes a significant impact on the structure complexity of the systems.
Construction of Optimal Zero Correlation Zone Aperiodic Complementary Sequence Sets
Xiaoyu CHEN, Heru SU, Xichao GAO
2021, 43(2): 461-466.   doi: 10.11999/JEIT190703
[Abstract](56) [FullText HTML](32) [PDF 530KB](9)
Abstract:
The construction of ZCZ Aperiodic Complementary Sequence (ZACS) sets are researched based on orthogonal matrices. The proposed approach can provide optimal ZACS sets and the length of ZCZ can be chosen flexibly under the condition of Z|N. The resultant sequence sets have ideal autocorrelation properties and intra-group complementary properties. By adjusting the parameter q, different ZACS sets can be obtained. Moreover, based on the multilevel perfect sequence over integer, Gaussian integer orthogonal matrix is constructed which can be used as the initial sequence in the construction of ZACS. The sequence sets can be applied to Multi-Carrier Code Division Multiple Access (MC-CDMA) system to remove multipath interference and multiple access interference. Furthermore, it can be used as training sequence in Multiple Input Multiple Output (MIMO) channel estimation.
Fast Public Key Searchable Encryption Scheme against Internal Keyword Guessing Attack in Cloud Environment
Ningjiang CHEN, Can LIU, Ruwei HUANG, Baohua HUANG
2021, 43(2): 467-474.   doi: 10.11999/JEIT190963
[Abstract](59) [FullText HTML](29) [PDF 1175KB](19)
Abstract:
With the development of cloud computing, the security and search performance of ciphertext retrieval has become the focus of research. In the traditional encryption schemes, most of them only solve the problem of defending against external keyword guessing attacks but ignore the honest and curious cloud server. In order to improve the security of ciphertext, an inside keyword attack scheme based on inverted index is proposed. Firstly, the private key of the data owner is added to resist the keyword attack of the malicious cloud server when the ciphertext inversion index is built. Secondly, an efficient public key ciphertext search scheme of parallel encryption index structure is introduced to realize the parallel search task of keywords. Compared with the traditional public key searchable encryption, the proposed scheme enhances greatly the security and search efficiency of the search system.
Recent Development of Theory and Application on Homomorphic Encryption
Yatao YANG, Yang ZHAO, Juanmei ZHANG, Jierun HUANG, Yuan GAO
2021, 43(2): 475-487.   doi: 10.11999/JEIT191019
[Abstract](639) [FullText HTML](212) [PDF 541KB](83)
Abstract:
With the popularization of various cloud services such as cloud computing and cloud storage, privacy preservation issues in the cloud environment have gradually become the focus of industrial applications. Homomorphic encryption has become an important method to solve this issue. Among them, how to construct an efficient fully homomorphic encryption scheme is one of the hotspots at present. Firstly, the development of homomorphic encryption is introduced. The homomorphic encryption schemes are analyzed and classified from different perspectives. The research progress of verifiable fully homomorphic encryption schemes is discussed in detail. By analyzing the property rights literature on homomorphic encryption that has been published in recent years, the progress in the theoretical research and application about homomorphic encryption are summarized. Secondly, the working performances of three typical homomorphic encryption libraries, Helib, SEAL and TFHE, are compared and analyzed. Finally, various application scenarios of homomorphic encryption technology are sorted out, and possible research and development directions in the future are proposed.
Application of Finite Element-Based Domain Decomposition Method to the Simulation for Permanent Magnet Focusing System
Peng XIE, Li XU, Junhui YIN, Zhonghai YANG, Bin LI
2021, 43(2): 488-494.   doi: 10.11999/JEIT190706
[Abstract](70) [FullText HTML](41) [PDF 1409KB](14)
Abstract:
With the development of computer technology and parallel solving technology, domain decomposition method has been increasingly applied to various fields of computational electromagnetics. For the simulation of microwave tube permanent magnet focusing system, this paper proposes a finite element-based non-overlapping domain decomposition method, and introduces a novel transmission condition. Then the interior penalty formulation is used to derive the finite element weak form. The biggest advantage of the proposed domain decomposition method is that no extra unknowns are introduced, and the final finite element matrix is symmetric and positive definite, which makes the matrix equation suitable be solved by the preconditioned conjugate gradient method. In this paper, several microwave tube permanent magnet focusing systems are simulated and compared with the commercial software Maxwell in detail. The results show that the proposed domain decomposition method has the same accuracy as Maxwell, but has a more superior computational performance.
A Brightness Temperature Reconstruction Method for Mirrored Aperture Synthesis Based on Truncated Singular Values
Yuanchao WU, Rongchuan LÜ, Haofeng DOU, Yi’nan LI, Hao LI, Hailiang LU, Feng LUO, Qingxia LI
2021, 43(2): 495-500.   doi: 10.11999/JEIT190886
[Abstract](367) [FullText HTML](142) [PDF 1570KB](14)
Abstract:
Image reconstruction for Mirrored Aperture Synthesis(MAS) radiometer is an image inversion process from cosine visibility function to brightness temperature, and the cosine visibility function is solved by the transformation equation. However, the transformation equation is ill-conditioned equation, and a small error in the correlation output causes a big deviation in the cosine visibility function. Therefore, the solution of the ill-conditioned equations is the key to the success of the brightness temperature reconstruction algorithm. Based on the basic principle of MAS, the ill-conditioned transformation matrix is analyzed, and the truncated singular value decomposition is applied to the solution of the transformation equation for MAS. Simulation and experiment show that this method can effectively reduce noise and improve image quality.
Two-segment Continuous Alignment and Error Analysis Method Based on Dual Antenna Assistance
Juhua YANG, Linjing ZHANG, Guangwu CHEN, Jianhao CHENG, Peng LI
2021, 43(2): 501-508.   doi: 10.11999/JEIT191021
[Abstract](86) [FullText HTML](51) [PDF 1419KB](14)
Abstract:
Considering the problem that it is difficult to obtain the initial position accurately in the low-precision Inertial Measurement Unit/Global Navigation Satellite System (IMU/GNSS) loose integrated navigation system and the course divergence is easy to travel. A two-segment continuous alignment method assisted by dual antennas is designed. Firstly, the influence of initial bearing error on heading accuracy is analyzed. Secondly, due to the characteristics of high accuracy and poor dynamic response of the GNSS direction-finding system, a least-squares attitude estimation model is derived based on the dual-antenna baseline vector for initial alignment. Finally, for the alignment between travels, the research extendes the one-dimensional measurement based on the heading difference to suppress the heading error. The design experiment explores the influence of the dual antenna baseline vector on the initial alignment and the heading accuracy between travels. The improved method can make the initial azimuth error better than 0.7°. At the same time, the heading angle between travels can be tracked more accurately. For the initial alignment of the target and the alignment between the traveling, the dual antenna can provide auxiliary information, its effect is better than the single antenna IMU/GNSS combination, and the method calculation is moderate.
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