Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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doi: 10.11999/JEIT200443
Abstract:
In view of the hotspot scenarios in B5G/6G, in order to meet the needs of its ultra-large network capacity, this paper constructs a multi-Unmanned Aerial Vehicle (UAV) assisted millimeter wave heterogeneous network model. In this network model, the distribution of Ground Base Station (G-BS) is modeled as Poisson point process, the distribution of UAV is modeled as Poisson cluster process, and the projection of the UAV on ground and the Ground User Equipment (GUE) are distributed around the G-BS. For the sake of exploring the contribution of inter-cluster association and the impact of inter-cluster interference, the 2-tier network model is extended to 4-tier network model composed of inter-cluster and intra-cluster Base Station (BS). And the 4-tier association scheme in which the GUE is associated with intra-cluster BS and inter-cluster BS at the same time is proposed. Initially, the path loss of each tier’s association distance is analyzed through the propagation model. Furthermore, using stochastic geometry method, combined with the interference of GUE in the downlink, the Signal-to-Interference plus Noise Ratio (SINR) coverage probability expression of GUE is derived. Finally, the simulation results show that the height of UAV and the average number of cluster members have non-monotonic effect on SINR coverage probability. When UAV height is low, compared with the 2-tier association scheme that GUE is only associated with the intra-cluster BS, the 4-tier association scheme proposed in this paper can improve the SINR coverage probability significantly.
doi: 10.11999/JEIT200785
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.
doi: 10.11999/JEIT200584
Abstract:
Time Delay Estimation(TDE) is an important research subject in underwater acoustic field. Passive direction-finding and passive location based on TDE are important brunches in passive location of underwater acoustic target. At present, the common TDE methods include Normalized Cross Correlation(NCC) method, Generalized Cross Correlation(GCC) method and Least Mean Square(LMS) method. Different from the above methods, a new method of TDE is designed by using Cross-power Spectrum Phase(CSP) in this paper. This method performs Fourier transform of CSP and uses the transform domain to estimate the CSP slope to get the time delay. This method can realize multi-target resolution and eliminate the ambiguity and background fluctuation. Simulation results show that this proposed method is better than NCC and GCC.
doi: 10.11999/JEIT200285
Abstract:
Generally, the refractive index of atmosphere is simply 1 by default in Synthetic Aperture Radar (SAR) imaging, that is, the ElectroMagnetic (EM) wave velocity is equal to the speed of light in free-space and the atmospheric absorption is ignored. However, the actual absorption may weaken the incident power and variations in the speed of EM waves may cause phase error, thus affecting image reconstruction. In this paper, the influence of wave velocity fluctuation and atmospheric absorption in SAR imagery is analyzed quantitatively. It is theoretically deduced that the atmospheric absorption will lead to amplitude error, which is shown as strength error of the scatterer in the reconstructed image; EM velocity fluctuation will lead to phase error, which is shown as positioning error of the scatterer in the reconstructed image. The correctness of error analysis is verified by simulation experiments. The work in this paper completes further the SAR imaging error analysis, which is beneficial to SAR image interpretation.
doi: 10.11999/JEIT200402
Abstract:
The matrix Constant False Alarm Rate (CFAR) detector based on the information geometry theory is an effective method for target detection in the K-distributed sea clutter environment. However, the general matrix CFAR method has a high computational complexity and its detection performance is not as good as ANMF when the target Doppler frequency deviates from the clutter spectrum center seriously, which affects its practical application. For this reason, considered the filtered received signal by the filter bank, a Matrix CFAR Detection method based on the Filter bank subband Decomposition of Maximum Eigenvalue (F-MEMD) is proposed. The double clutter suppression helps to solve the problem that Matrix CFAR is invalid when the target Doppler frequency is far away the central of the clutter spectrum. Finally, the simulation results show that the improved F-MEMD has a good detection performance.
doi: 10.11999/JEIT200505
[Abstract](88) [FullText HTML](31) [PDF 1889KB](15)
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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.
doi: 10.11999/JEIT200704
[Abstract](16) [FullText HTML](8) [PDF 6057KB](4)
Abstract:
To better understand and utilize the acoustic field in shallow sea, a theoretical method that can give the full-wave solution is proposed, then the complex integral expression of the acoustic field is given. The complex integral fraction is solved in the complex plane, and the components of the acoustic field in shallow sea are obtained. The high-order staggered grid finite difference method is also used to numerically simulate the acoustic field in shallow sea, showing the wave field structure and spatial energy distribution. Results show that the acoustic field in shallow sea includes discrete spectrum and continuous spectrum; the discrete spectrum includes normal waves and Scholte wave, and the continuous wave includes lateral waves; the amplitudes of normal waves and Scholte wave are inversely proportional to the root of horizontal propagation distance, and the amplitude of lateral wave is inversely proportional to the power of horizontal propagation distance; the shallower the sea water, the lower the frequency and the greater the depth of the sound source, the less energy in the sea water will be, the energy radiated by the acoustic source is mainly propagated in the form of Scholte wave, and the energy is mostly concentrated at the seabed interface.
doi: 10.11999/JEIT200657
[Abstract](11) [FullText HTML](5) [PDF 8361KB](2)
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In order to obtain higher resolution and avoid the failure of Two-Dimensional (2D) of Direction-Of-Arrival (DOA) estimation, One-Dimensional (1D) spatial DOA estimation method, vertical DOA estimation via Vernier method and horizontal DOA estimation method via minimum angle theorem are proposed. First, covariance matrices are constructed based on various subarrays to alleviate the failure of 2D model, and the Khatri-Rao product is adopted to extend the virtual array aperture. Second, the extended observation models and corresponding array steer vector are exploited for 2D DOA estimation. Compared with the steer vector of the original array, the number of virtual array elements is doubled, and thus the array aperture is extended. Simulation results show that the proposed method has better resolution and lower RMSE performance in 2D DOA estimation problem compared with the Single Measured Vector Beamforming method. The tank experiment further verified the engineering practicability of the proposed method.
doi: 10.11999/JEIT200650
[Abstract](13) [FullText HTML](7) [PDF 2720KB](6)
Abstract:
In the case of diving highly squinted Synthetic Aperture Radar (SAR), the existence of range-dependent squint angle, severe range-azimuth coupling, three-Dimensional (3-D) velocity and acceleration produces two-Dimensional (2-D) spatial-variant Range Cell Migration (RCM) and Doppler phases. To accommodate these issues, this paper constructs a 3-D equidistant sphere analytical model to precisely reveal the range-azimuth variant property of the echo. Based on the model, an azimuth-variant residual high-order RCM correction is proposed, and the Frequency Extended NonLinear Chirp Scaling (FENLCS) is rederived to equalize the azimuth-variant Doppler phases. These two methods integrated with SubAperture (SA) processing are adopted to address the aforementioned issues faced by diving highly squinted SAR. Theoretical analysis and simulation results validate that the proposed model is capable of describing the range-azimuth spatial-variance property of echo more precisely, and better imaging performance can be acquired by this algorithm.
doi: 10.11999/JEIT200649
[Abstract](28) [FullText HTML](15) [PDF 1841KB](9)
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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.
doi: 10.11999/JEIT200582
[Abstract](46) [FullText HTML](27) [PDF 1440KB](12)
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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.
doi: 10.11999/JEIT200541
[Abstract](33) [FullText HTML](14) [PDF 1132KB](6)
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In order to realize the fast direction estimation of underwater targets under the conditions of less snapshot and low SNR, a sparse decomposition model of vector hydrophone array direction estimation is established. The real value conversion technique is used to convert the complex direction matrix into the real number field, so as to reconstruct the sparse signal matrix using the SL0 algorithm to obtain the orientation estimation result. Improve the SL0 algorithm, use the Compound Inverse Proportional Function (CIPF) function with better convergence as a smoothing function, and propose a weighted method which can promote sparsity, use the weighted method to correct the problem that the norm as the initial iteration value deviates far from the sparse solution to increase the speed of azimuth estimation. The simulation verifies that the proposed algorithm can achieve better performance than the traditional subspace algorithm under the conditions of low snapshot and low SNR, and Improve the speed of bearing estimation while ensuring performance.
doi: 10.11999/JEIT200449
[Abstract](71) [FullText HTML](22) [PDF 3386KB](15)
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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.
doi: 10.11999/JEIT200315
[Abstract](56) [FullText HTML](17) [PDF 3768KB](12)
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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.
doi: 10.11999/JEIT200894
[Abstract](13) [FullText HTML](6) [PDF 2056KB](1)
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For the most of the existing gesture recognition methods based on the radar sensor, the parameters such as the distance, Doppler, and angle are estimated using the radar echo at first. And then the according data spectra are inputted into the convolutional neural networks to classify the gestures. The implementation process is complicated. A dynamic gesture recognition method is proposed based on the millimeter-wave radar using the one-Dimensional Series connection Neural Networks (1D-ScNN) in this paper. Firstly, the original echo of dynamic gesture is obtained by the millimeter-wave radar. The gesture features are extracted by the one-dimensional convolution and pooling operations, and then are inputted into the one-dimensional inception v3 structure. In order to aggregate the one-dimensional features, the Long Short-Term Memory (LSTM) modular is connected to the end of the network. The inter-frame correlation of dynamic gestures echo is fully utilized to improve the recognition accuracy and the convergence speed of training. The experimental results show that the proposed method is simple in implementation and has a fast convergence speed. The classification accuracy can reach more than 96.0%, which is higher than the traditional gesture classification methods.
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2021, 43(2): 0-0.
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2021, (2): 1-4.
[Abstract](7) [PDF 252KB](2)
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2021, 43(2): 255-262.   doi: 10.11999/JEIT200142
[Abstract](58) [FullText HTML](43) [PDF 2775KB](13)
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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.
2021, 43(2): 263-269.   doi: 10.11999/JEIT200288
[Abstract](44) [FullText HTML](38) [PDF 1622KB](11)
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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.
2021, 43(2): 270-276.   doi: 10.11999/JEIT200130
[Abstract](80) [FullText HTML](24) [PDF 1163KB](12)
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2021, 43(2): 277-283.   doi: 10.11999/JEIT200342
[Abstract](64) [FullText HTML](34) [PDF 2223KB](22)
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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.
2021, 43(2): 284-288.   doi: 10.11999/JEIT200344
[Abstract](205) [FullText HTML](77) [PDF 1348KB](34)
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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.
2021, 43(2): 289-295.   doi: 10.11999/JEIT200358
[Abstract](89) [FullText HTML](33) [PDF 1722KB](9)
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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.
2021, 43(2): 296-302.   doi: 10.11999/JEIT190793
[Abstract](61) [FullText HTML](32) [PDF 3050KB](5)
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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.
2021, 43(2): 303-309.   doi: 10.11999/JEIT200371
[Abstract](107) [FullText HTML](63) [PDF 1161KB](40)
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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.
2021, 43(2): 310-318.   doi: 10.11999/JEIT200113
[Abstract](57) [FullText HTML](26) [PDF 2083KB](10)
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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.
2021, 43(2): 319-328.   doi: 10.11999/JEIT190920
[Abstract](33) [FullText HTML](16) [PDF 2388KB](10)
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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.
2021, 43(2): 329-334.   doi: 10.11999/JEIT191006
[Abstract](51) [FullText HTML](28) [PDF 914KB](7)
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2021, 43(2): 335-340.   doi: 10.11999/JEIT191003
[Abstract](54) [FullText HTML](26) [PDF 1843KB](6)
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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.
2021, 43(2): 341-348.   doi: 10.11999/JEIT191031
[Abstract](83) [FullText HTML](30) [PDF 1752KB](30)
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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.
2021, 43(2): 349-355.   doi: 10.11999/JEIT200018
[Abstract](226) [FullText HTML](72) [PDF 4036KB](25)
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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.
2021, 43(2): 356-363.   doi: 10.11999/JEIT190694
[Abstract](66) [FullText HTML](18) [PDF 2354KB](12)
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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.
2021, 43(2): 364-371.   doi: 10.11999/JEIT190757
[Abstract](38) [FullText HTML](16) [PDF 2844KB](4)
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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.
2021, 43(2): 372-379.   doi: 10.11999/JEIT191048
[Abstract](8) [PDF 0KB](0)
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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.
2021, 43(2): 380-387.   doi: 10.11999/JEIT190644
[Abstract](34) [FullText HTML](17) [PDF 2217KB](5)
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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.
2021, 43(2): 388-395.   doi: 10.11999/JEIT190971
[Abstract](637) [FullText HTML](260) [PDF 2871KB](73)
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.
2021, 43(2): 396-403.   doi: 10.11999/JEIT191039
[Abstract](151) [FullText HTML](54) [PDF 1116KB](18)
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.
2021, 43(2): 404-411.   doi: 10.11999/JEIT190921
[Abstract](79) [FullText HTML](19) [PDF 2046KB](25)
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.
2021, 43(2): 412-418.   doi: 10.11999/JEIT191015
[Abstract](76) [FullText HTML](41) [PDF 2007KB](21)
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%.
2021, 43(2): 419-425.   doi: 10.11999/JEIT190878
[Abstract](46) [FullText HTML](24) [PDF 2139KB](11)
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.
2021, 43(2): 426-435.   doi: 10.11999/JEIT190803
[Abstract](95) [FullText HTML](41) [PDF 3495KB](41)
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.
2021, 43(2): 436-444.   doi: 10.11999/JEIT191002
[Abstract](56) [FullText HTML](30) [PDF 1902KB](12)
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.
2021, 43(2): 445-453.   doi: 10.11999/JEIT190955
[Abstract](74) [FullText HTML](15) [PDF 2486KB](10)
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.
2021, 43(2): 454-460.   doi: 10.11999/JEIT190875
[Abstract](48) [FullText HTML](29) [PDF 1977KB](4)
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.
2021, 43(2): 461-466.   doi: 10.11999/JEIT190703
[Abstract](42) [FullText HTML](29) [PDF 530KB](7)
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.
2021, 43(2): 467-474.   doi: 10.11999/JEIT190963
[Abstract](46) [FullText HTML](24) [PDF 1175KB](10)
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.
2021, 43(2): 475-487.   doi: 10.11999/JEIT191019
[Abstract](610) [FullText HTML](196) [PDF 541KB](76)
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.
2021, 43(2): 488-494.   doi: 10.11999/JEIT190706
[Abstract](56) [FullText HTML](33) [PDF 1409KB](11)
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.
2021, 43(2): 495-500.   doi: 10.11999/JEIT190886
[Abstract](348) [FullText HTML](134) [PDF 1570KB](13)
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.
2021, 43(2): 501-508.   doi: 10.11999/JEIT191021
[Abstract](74) [FullText HTML](43) [PDF 1419KB](10)
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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