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For LEO Satellite Networks: Intelligent Interference Sensing and signal reconstruction Based on Blind Separation Technology

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China Communications 2024年第2期21卷 85-95页

作者： Chengjie Li Lidong Zhu Zhen Zhang The Key Laboratory for Computer Systems of State Ethnic Affairs Commission School of Computer Science and Technology(Southwest Minzu University)Chengdu 610000China National Key Laboratory of Science and Technology on Communications(University of Electronic Science and Technology of China) Chengdu 610000China College of Computer Science(Sichuan University) Chengdu 610000China

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

关键词： blind source separation greedy optimization algorithm interference sensing LEO satellite communication networks signal reconstruction

signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

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Chinese Physics B 2014年第7期23卷 311-315页

作者：黄锦旺冯久超 School of Electronic and Information Engineering South China University of Technology

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks （WSNs）, a new signal reconstruction algorithm based on a cubature Kalman particle filter （CKPF） is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter （CKF） to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter （UKPF） and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

关键词： cubature rule particle filter signal reconstruction chaotic signals

Nonuniform Three-dimensional Configuration Distributed SAR signal reconstruction Clutter Suppression

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Chinese Journal of Aeronautics 2012年第3期25卷 423-429页

作者： LIU Mei ZHANG Lei LI Chenlei School of Electronics Information Engineering Harbin Institute of TechnologyHarbin 150001China

This paper deals with the problem of clutter suppression in spaceborne distributed synthetic aperture radar （D-SAR） with nonuniform three-dimensional （3D） configuration geometry. In order to make a breakthrough of the configuration limitation of the traditional space time adaptive processing （STAP） based on uniform array and improve the inhomogeneous clutter suppres- sion performance, this paper considers signal reconstrtiction technique using array interpolation to process the D-SAR signal. An array interpolation signal reconstruction method based on pitching-partition is derived then a signal reconstruction 3D-STAP clutter suppression method applied to nonuniform 3D configuration is proposed. In particular, the proposed method is compared with conventional methods and the performance analysis is carried out based on simulations. The improvement factor （IF） for clutter suppression is imported and reported as a benchmark on the clutter suppression effect.

关键词： distributed synthetic aperture radar nonunitform 3D configuration clutter suppression signal reconstruction STAP

A sparsity adaptive compressed signal reconstruction based on sensing dictionary

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Journal of Systems Engineering and Electronics 2021年第6期32卷 1345-1353页

作者： SHEN Zhiyuan WANG Qianqian CHENG Xinmiao College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 210016China Zhejiang Scientific Research Institute of Transport Hangzhou 310023China Civil Aviation Branch Jiangsu Transportation InstituteNanjing 210003China

signal reconstruction is a significantly important theoretical issue for compressed *** the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low *** this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction *** sparsity estimation method is combined with the construction of the support set based on sensing *** the adaptive sparsity method,an iterative signal reconstruction algorithm is *** sufficient conditions for the exact signal reconstruction of the algorithm also is proved by *** to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.

关键词： compressed sensing signal reconstruction adaptive sparsity estimation sensing dictionary

Research on the signal reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

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Computers, Materials & Continua 2019年第2期58卷 409-420页

作者： Yajie Sun Yanqing Yuan Qi Wang Lihua Wang Enlu Li Li Qiao Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information and Control Nanjing University of Information Science and TechnologyNanjing210044China Capability Systems Centre School of Engineering and Information TechnologyUniversity of New South WalesCanberra2600Australia

The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring *** method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array *** to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse *** sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse ***,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried *** research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.

关键词： Basis pursuit algorithm compressive sensing phased array signal reconstruction

Sensing Matrix Optimization for Random Stepped-Frequency signal Based on Two-Dimensional Ambiguity Function

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Chinese Journal of Electronics 2024年第1期33卷 161-174页

作者： Mingjiu LYU Hao CHEN Jun YANG Xia WU Ming ZHOU Xiaoyan MA Radar NCO School Air Force Early Warning Academy Department of Early Warning Technology Air Force Early Warning Academy

Compressive sensing technique has been widely applied to achieve range-Doppler reconstruction of high frequency radar by utilizing sparse random stepped-frequency(SRSF) signal, which can suppress the complex electromagnetic interference and greatly reduce the coherent processing interval. An important way to improve the performance of sparse signal reconstruction is to optimize the sensing matrix(SM). However, the existing research on the SM optimization needs to design a measurement matrix with superior performance, which needs a large amount of computation and does not consider the influence of the waveform parameters design. In order to improve the superior reconstruction performance, a novel SM optimization approach for SRSF signal is proposed by using two-dimensional ambiguity function(TDAF) in this paper. Firstly, based on the two-dimensional sparse reconstruction model of the SRSFs, the internal relationship between the waveform parameters and the SM was derived. Secondly, the SM optimization problem was directly transformed into the waveform design of SRSFs. Furthermore, on the basis of analyzing the relationship between the mutual coherence matrix of SM and the TDAF matrix of SRSFs, the purpose of optimizing the SM can be achieved by designing the TDAF of the SRSFs. Based on this analysis, a sparse waveform optimization method with joint constraints of maximum and mean sidelobes of the TDAF by using the genetic algorithm was derived. Compared with the traditional SM optimization method, our method not only avoids generating a new measurement matrix, but also further reduces the complexity of the waveform optimization. Simulation experiments verified the effectiveness of the proposed method.

关键词： Correlation Computational modeling Electromagnetic interference Optimization methods signal reconstruction High frequency radar Sensors

ON DIRECT METHODS FOR TIME-LIMITED signal AND IMAGE reconstruction AND ENHANCEMENT

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ON DIRECT METHODS FOR TIME-LIMITED SIGNAL AND IMAGE RECONSTR...

Civil aircraft fault tolerant attitude tracking based on extended state observers and nonlinear dynamic inversion

中国科学院地质与地球物理研究所2007学术年会

作者： ZUHAIR NASHED Department of Mathematics University of Central Florida FL 32816USA

The discrete Fourier transform(DFT) can be considered as an observing system,which has an input f,an output F,and a response with additive noise *** many applications, part of the frequency spectrum/frequency information is missing or unavailable due to the passage of the time-limited signal through a band-limited system,for example,the discrete Fourier *** suggest improving the resolution of the reconstruction of signals and images using a novel approach for the solution of the discrete Fourier system and by image *** note that the reconstruction of a time-limited signal can be simply realized by only using either the real part or the imaginary part of the DFT ***,based on the study of the special structure of the real and imaginary parts of the discrete Fourier matrix,a fast direct computational method is developed that utilizes explicit formulas for the truncated singular value decomposition(TSVD) obtained recently by the *** improving the resolution of the reconstructions, enhancement by logarithm transform is *** fast direct computational method is superior to other direct methods such as LU decomposition,QR decomposition,classical SVD and classical *** explicit TSVD along with the enhancement can be considered as a useful tool for signal and image *** tests for signal and image reconstructions and enhancements are given as well.

关键词： signal reconstruction discrete Fourier transform singular value decomposition signal and image enhancement

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Journal of Systems Engineering and Electronics 2022年第1期33卷 180-187页

作者： MA Xinjian LIU Shiqian CHENG Huihui School of Aeronautics and Astronautics Shanghai Jiao Tong UniversityShanghai 200240China

For the problem of sensor faults and actuator faults in aircraft attitude control,this paper proposes a fault tolerant control(FTC)scheme based on extended state observer(ESO)and nonlinear dynamic inversion(NDI).First,two ESOs are designed to estimate sensor faults and actuator faults ***,the angular rate signal is reconstructed according to the estimation of sensor ***,in angular rate loop,NDI is designed based on reconstruction of angular rate signals and estimation of actuator *** FTC scheme proposed in this paper is testified through numerical *** results show that it is feasible and has good fault tolerant ability.

关键词： fault tolerant control(FTC) signal reconstruction extended state observer(ESO) nonlinear dynamic inversion(NDI)

signal recovery method based on co-prime array

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Journal of Systems Engineering and Electronics 2017年第2期28卷 224-234页

作者： Shuang Qiu Weixing Sheng Xiaofeng Ma Yubing Han Renli Zhang School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China

A signal waveform recovery method based on the co-prime array is investigated to extract the waveform of the desired signal from spatial interferences in narrowband scenarios. The direction of arrivals (DOAs) of the desired signal and interference signals are estimated with the compressive sensing approach based on angle grids, and the signal power together with the noise power are estimated. Thereafter, a modified steepest descent (SD) method is derived to recover the waveform of the desired signal and interferences utilizing the estimated power and directions. The recovered waveform of the desired signal is the output of the proposed method. The situation in which the signals are not on the predefined angle grids is also considered. The DOAs estimated via the predefined angle grids are corrected based on the maximum likelihood (ML) angle estimation. Compared to the existing beamforming methods on co-prime array, the proposed method can obtain the waveform of the desired signal. Simulation results demonstrate that the proposed method can achieve good performance in signal waveform recovery and output signal to noise ratio. © 2017 Beijing Institute of Aerospace Information.

关键词： Compressed sensing Maximum likelihood Maximum likelihood estimation Recovery signal reconstruction signal to noise ratio