一种低秩联合稀疏模型下的杂波抑制方法

doi:10.19665/j.issn1001-2400.2019.06.009

摘要/Abstract

摘要：

针对现有穿墙雷达探测研究中,墙体回波抑制算法需要采集全部雷达回波导致运算复杂的问题,提出了一种低秩联合稀疏模型下的墙体回波抑制方法。在提出的方法中,从雷达回波中分离墙体回波和目标回波的任务被转化为一个低秩保持和稀疏约束的优化模型,并使用交替方向乘子法求解该优化模型;最后分离出墙体回波后的回波信号被用于雷达成像。仿真实验结果表明,该方法对各种情况下的墙体回波都能有效去除。与当前经典的奇异值分解、迭代软阈值方法相比,所提出的杂波抑制方法在穿墙雷达成像中具有更高的目标杂波比。

关键词: 穿墙雷达, 杂波抑制, 低秩, 稀疏重构

Abstract:

The existing method for suppression of clutters of the through-the-wall radar system requires full size data, which may increase the complexity of the system. To this end, a wall clutter suppression method with the joint low-rank and sparse model is developed in this paper. In the proposed method, the task of separating wall clutters and target returns is transformed into a low-rank and sparse constrained optimization model requiring less data. To solve this optimization, the alternating direction method for multipliers is adopted. After clutter suppression, the return signals are used for the image formation. Experimental results demonstrate that the proposed method is significantly effective on clutter suppression in different scenes. Compared with the existing methods such as singular value decomposition and iterative soft thresholding, the proposed method has a higher target-to-clutter ratio in the radar imaging results.

Key words: through-wall-radar, clutter mitigation, low-rank, sparse reconstruction

中图分类号:

TN957.52

黄臣,刘宏清,罗臻,周翊. 一种低秩联合稀疏模型下的杂波抑制方法[J]. 西安电子科技大学学报, 2019, 46(6): 60-66.

HUANG Chen,LIU Hongqing,LUO Zhen,ZHOU Yi. Method for suppressing clutters with the joint low-rank and sparse model[J]. Journal of Xidian University, 2019, 46(6): 60-66.

图/表 8

图1

图2

表1

表2

表3

图3

图4

图5

参考文献 19

[1]	AMIN M G. 穿墙雷达成像[M]. 北京: 电子工业出版社, 2014: 54-72.
[2]	GUO S S, CUI G L, KONG L J , et al. An Imaging Dictionary Based Multipath Suppression Algorithm for Through-wall Radar Imaging[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018,54(1):269-283. doi: 10.1109/TAES.2017.2756298
[3]	VISHWAKARMA S, UMMALANENI V, IQBAL M S , et al. Mitigation of Through-wall Interference in Radar Images Using Denoising Autoencoders[C]//Proceedings of the 2018 IEEE Radar Conference. Piscataway: IEEE, 2018: 1543-1548.
[4]	WANG X Q, LI G, LIU Y , et al. Two-level Block Matching Pursuit for Polarimetric Through-wall Radar Imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018,56(3):1533-1545. doi: 10.1109/TGRS.2017.2764920
[5]	LI H Q, CUI G L, KONG L J , et al. Robust Human Targets Tracking for MIMO Through-wall Radar via Multi-Algorithm Fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019,12(4):1154-1164. doi: 10.1109/JSTARS.4609443
[6]	ZHAO G L, LIANG Q L, DURRANI T S . UWB Radar Target Detection Based on Hidden Markov Models[J]. IEEE Access, 2018,6:28702-28711. doi: 10.1109/ACCESS.2018.2839690
[7]	CHANDRA R, GAIKWAD A N, SINGH D , et al. An Approach to Remove the Clutter and Detect the Target for Ultra-wideband Through-wall Imaging[J]. Journal of Geophysics and Engineering, 2008,5(4):412-419. doi: 10.1088/1742-2132/5/4/005
[8]	VERMA P K, GAIKWAD A N, SINGH D , et al. Analysis of Clutter Reduction Techniques for Through Wall Imaging in UWB Range[J]. Progress In Electromagnetics Research, 2009,17:29-48.
[9]	TIVIVE F H C, BOUZERDOUM A, AMIN M G. An SVD-based Approach for Mitigating Wall Reflections in Through-the-wall Radar Imaging[C]//Proceedings of the 2011 IEEE National Radar Conference. Piscataway: IEEE, 2011: 519-524.
[10]	AHMAD F, QIAN J, AMIN M G . Wall Clutter Mitigation Using Discrete Prolate Spheroidal Sequences for Sparse Reconstruction of Indoor Stationary Scenes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(3):1549-1557. doi: 10.1109/TGRS.36
[11]	ZHU Z H, WAKIN M B . On the Dimensionality of Wall and Target Return Subspaces in Through-the-wall Radar Imaging[C]//Proceedings of the 2016 4th International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing. Piscataway: IEEE, 2016: 110-114.
[12]	TANG V H, BOUZERDOUM A, PHUNG S L , et al. Radar Imaging of Stationary Indoor Targets Using Joint Low-rank and Sparsity Constraints[C]//Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2016: 1412-1416.
[13]	BOUZERDOUM A, TANG V H, PHUNG S L . A Low-rank and Jointly-sparse Approach for Multipolarization Through-wall Radar Imaging[C]//Proceedings of the 2017 IEEE Radar Conference. Piscataway: IEEE, 2017: 0263-0268.
[14]	TANG V H, BOUTERDOUM A, PHUNG S L . A Matrix Completion Approach for Wall-clutter Mitigation in Compressive Radar Imaging of Indoor Targets[C]//Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2018: 1608-1612.
[15]	景国彬, 李宁, 孙光才 , 等. 联合误差估计的机载超高分辨率SAR成像[J]. 西安电子科技大学学报, 2019,46(3):1-7.
	JING Guobin, LI Ning, SUN Guangcai , et al. Very High Resolution SAR Imaging Method Combined with Motion Estimation[J]. Journal of Xidian University, 2019,46(3):1-7.
[16]	BOYD S, PARIKH N, CHU E , et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers[J]. Foundations and Trends in Machine learning, 2010,3(1):1-122. doi: 10.1561/2200000016
[17]	BECKER S R, CANDèS E J, GRANT M C . Templates for Convex Cone Problems with Applications to Sparse Signal Recovery[J]. Mathematical Programming Computation, 2011,3(3):165-218. doi: 10.1007/s12532-011-0029-5
[18]	MAZUMDER R, HASTIE T, TIBSHIRANI R . Spectral Regularization Algorithms for Learning Large Incomplete Matrices[J]. Journal of Machine Learning Research, 2010,11:2287-2322. pmid: 21552465
[19]	WARREN C, GIANNOPOULOS A, GIANNAKIS I . gprMax: Open Source Software to Simulate Electromagnetic Wave Propagation for Ground Penetrating Radar[J]. Computer Physics Communications, 2016,209:163-170.

墙体材质	目标杂波比/dB
木材,ε_w=2.1	17.301
木材,ε_w=3.4	17.241
细砂砾,ε_w=3.7	17.217
混凝土,ε_w=5.3	17.043
混凝土,ε_w=6	16.981

墙体厚度/m	目标杂波比/dB
0.10	16.981
0.15	16.606
0.20	15.833
0.30	15.594
0.40	12.751
0.60	8.451

墙体结构	目标杂波比/dB
空心砖结构	16.359
钢筋混凝土结构	16.513
双层结构	16.672
三层结构	15.416