Suppressive Interference Suppression for Airborne SAR Using BSS for Singular Value and Eigenvalue Decomposition Based on Information Entropy

被引：7
作者：
Chen, Si [1 ]
Lin, Yang [1 ]
Yuan, Yue [1 ]
Li, Xiaoxiong [1 ]
Hou, Linsheng [1 ]
Zhang, Shuning [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210094, Peoples R China
来源：
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2023年 / 61卷
基金：
中国国家自然科学基金;
关键词：
Interference; Eigenvalues and eigenfunctions; Information entropy; Synthetic aperture radar; Jamming; Blind source separation; Imaging; Blind source separation (BSS); information entropy; suppressive interference suppression; synthetic aperture radar (SAR); RFI SUPPRESSION; SPARSE; SEPARATION; EFFICIENT;
D O I：
10.1109/TGRS.2023.3263218
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
Suppressive interference is a common interference signal for synthetic aperture radar (SAR) that can seriously affect the target identification and imaging results of SAR. This article proposes a method for suppressing suppressive jamming using blind source separation (BSS) for singular value decomposition (SVD) and eigenvalue decomposition (EVD) based on information entropy. First, we developed an airborne SAR imaging geometry model and a suppressive interference signal mixing model. Next, we perform blind signal separation of the interfered mixed signal by means of BSS based on SVD and EVD. Then, we image the different signals we have extracted. Finally, we extract the features of the image domain for the separated signals and set the information entropy threshold by the difference of information entropy to identify the jamming signal and the source signal and obtain the source signal. This method uses EVD and SVD for BSS and extracts the image domain features of the signal after BSS by information entropy and identifies the source signal by information entropy thresholding. This method compensates for the uncertainty in the decomposition of the signal by means of BSS. The signal loss is minimal and the similarity of the separated signal and the original signal is very high. Simulated and measured data demonstrate the feasibility of this algorithm.
引用
收藏
页数：11
相关论文
共 32 条
  • [1] Ahmad M., 2014, PROC INT C ENERGY SY, P1
  • [2] [Anonymous], 1998, PROC 7 ANN JPL AIRBO
  • [3] Regularized Gaussian discriminant analysis through eigenvalue decomposition
    Bensmail, H
    Celeux, G
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1996, 91 (436) : 1743 - 1748
  • [4] Cazzaniga G, 1996, INT GEOSCI REMOTE SE, P1845
  • [5] Multi-Electromagnetic Jamming Countermeasure for Airborne SAR Based on Maximum SNR Blind Source Separation
    Chen, Si
    Yuan, Yue
    Wang, Sixiang
    Yang, Huanhuan
    Zhu, Lingzhi
    Zhang, Shuning
    Zhao, Huichang
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [6] Study of ultra-wideband fuze signal processing method based on wavelet transform
    Chen, Si
    Zhao, Huichang
    Zhang, Shuning
    Yang, Yunxing
    [J]. IET RADAR SONAR AND NAVIGATION, 2014, 8 (03) : 167 - 172
  • [7] A robust interference cancellation technique for DS-UWB systems using fuzzy step size LMS algorithm
    Cheng, Chia-Hsin
    Wen, Jyh-Horng
    Chen, Yu-Fan
    Lin, Jen-Yung
    [J]. EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS, 2008, 19 (02): : 207 - 217
  • [8] Chenyang Zhang, 2019, 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE), P10, DOI 10.1109/ICISCAE48440.2019.221578
  • [9] Natural gradient learning for second-order nonstationary source separation
    Choi, S
    Cichocki, A
    Amari, S
    [J]. PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 654 - 658
  • [10] Gradual RELAX algorithm for RFI suppression in UWB-SAR
    Huang, X
    Liang, D
    [J]. ELECTRONICS LETTERS, 1999, 35 (22) : 1916 - 1917
1234