利用块间耦合稀疏贝叶斯学习的建筑物布局成像方法

晋良念; 冯飞; 刘庆华; 欧阳缮

doi:10.11999/JEIT170719

利用块间耦合稀疏贝叶斯学习的建筑物布局成像方法

doi: 10.11999/JEIT170719

2.
(桂林电子科技大学信息与通信学院桂林 541004)

基金项目:

国家自然科学基金(61461012)，广西自然科学基金(2017GXNSFAA198050)，广西无线宽带通信与信号处理重点实验室2016主任基金(GXKL06160106)

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出版历程
- 收稿日期: 2017-07-19
- 修回日期: 2017-12-25
- 刊出日期: 2018-04-19

Building Layout Imaging Method Using the Inter-block Coupling Sparse Bayesian Learning

2.
(Institute of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China)

Funds:

The National Natural Science Foundation of China (61461012), Guangxi Natural Science Foundation (2017GXNSFAA198050), Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, 2016 the Fund Project of Diretor (GXKL06160106)

摘要

摘要: 该文针对现有穿墙雷达建筑物布局成像中扩展目标稀疏成像方法未能有效利用墙体反射信号的结构稀疏性，导致成像中出现墙体不连贯和墙体轮廓不明显的问题，提出一种利用稀疏信号块间耦合的建筑物布局成像方法。该方法在块稀疏信号特性的高斯分层先验模型的基础上，利用块间耦合系数进一步表征场景中墙体反射信号的结构稀疏性，然后将其引入到控制稀疏信号先验概率分布的超参数中，从而把稀疏信号的结构性转化为超参数的耦合关系，最后利用期望最大化(EM)算法求解超参数的最大后验(MAP)估计。仿真和实验数据处理结果表明，该方法有效改善了墙体的成像质量。
- 穿墙雷达 /
- 建筑物布局成像 /
- 结构稀疏性 /
- 稀疏贝叶斯学习 /
- 块间耦合
Abstract: In through-wall radar building layout imaging, the existing extended target sparse imaging method can not effectively exploit the structural sparsity of the wall reflections in the scene, resulting in incoherent imaging and unobvious contour of walls. A sparse Bayesian learning method is proposed for building layout imaging by exploiting the inter-block coupling of sparse signal. On the basis of the hierarchical Gaussian prior model of block sparse signal characteristics, the inter-block coupling coefficient is further used to characterize the structured sparsity of the wall reflections. Then these coefficients are introduced into the hyperparameters controlling the prior distribution of sparse signal, thus this structured sparsity is transformed into the coupling relationship of these hyperparameters. Susequently, an Expectation-Maximization (EM) algorithm is developed to infer the Maximum A Posterior (MAP) estimate of these hyperparameters. The results of simulation and experiment show that the proposed method improves effectively the imaging quality of the building wall.
- Through-wall radar /
- Building layout imaging /
- Structured sparsity /
- Sparse Bayesian learning /
- Inter- block coupling

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参考文献(17)

BOUZERDOUM A, TANG V H, and PHUNG S L. A low-rank and jointly-sparse approach for multipolarization through-wall radar imaging[C]. IEEE Radar Conference (RadarConf), Seattle, 2017: 0263-0268. doi: 10.1109/ RADAR.2017.7944209.

WANG Xueqian, LI Gang, Wan Qun, et al. Look-ahead hybrid matching pursuit for multipolarization through-wall radar imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7): 4072-4081. doi: 10.1109/TGRS. 2017.2687478.

JIA Yong, CUI Guolong, KONG Lingjiang, et al. Multichannel and multiview imaging approach to building layout determination of through-wall radar[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(5): 970-974. doi: 10.1109/LGRS.2013. 2283778.

LU Biying, JIN Tian, WANG Wu, et al. Building layout imaging in through-the-wall MIMO applications[C]. IET International Radar Conference, Hangzhou, 2015: 1-5. doi: 10.1049/cp.2015.1326.

LAGUNAS E, AMIN M G, AHMAD F, et al. Determining building interior structures using compressive sensing[J]. Journal of Electronic Imaging, 2013, 22(2): 021003. doi: 10.1117/1.JEI.22.2. 021003.

ZHANG Zhilin and RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912-926. doi: 10.1109/JSTSP.2011. 2159773.

FANG Jun, SHEN Yanning, LI Hongbin, et al. Pattern- coupled sparse Bayesian learning for recovery of block-sparse signals[J]. IEEE Transactions on Signal Processing, 2015, 63(2): 360-372. doi: 10.1109/TSP.2014.2375133.

WU Qisong, ZHANG Yimin, AMIN M G, et al. Multi-task bayesian compressive sensing exploiting intra-task dependency[J]. IEEE Signal Processing Letters, 2015, 22(4): 430-434. doi: 10.1109/LSP.2014.2360688.

DUAN Huiping, ZHANG Lizao, FANG Jun, et al. Pattern-coupled sparse Bayesian learning for inverse synthetic aperture radar imaging[J]. IEEE Signal Processing Letters, 2015, 22(11): 1995-1999. doi: 10.1109/LSP.2015. 2452412.

张燕, 晋良念. 结合TV约束的穿墙雷达扩展目标成像方法[J]. 雷达科学与技术, 2017, 15(3): 229-235. doi: 10.3969/ j.issn.1672-2337.2017.03.001.

ZHANG Yan and JIN Liangnian. Extended target through walls radar imaging with TV constraints[J]. Radar Science and Technology, 2017, 15(3): 229-235. doi: 10.3969/j.issn. 1672-2337.2017.03.001.

CHEN Yijun, ZHANG Qun, LUO Ying, et al. Measurement matrix optimization for ISAR sparse imaging based on genetic algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1875-1879. doi: 10.1109/LGRS.2016. 2616352.

ZHANG Zhilin and RAO B D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation[J]. IEEE Transactions on Signal Processing, 2013, 61(8): 2009-2015. doi: 10.1109/TSP.2013.2241055.

TIVIVE F H C, BOUZERDOUM A, and AMIN M G. A subspace projection approach for wall clutter mitigation in through-the-wall radar imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2108-2122. doi: 10.1109/TGRS.2014. 2355211.

YANG Jungang, HUANG Xiaotao, THOMPSON J, et al. Compressed sensing radar imaging with compensation of observation position error[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4608-4620. doi: 10.1109/TGRS.2013. 2283054.

晋良念, 申文婷, 钱玉彬, 等. 组合字典下超宽带穿墙雷达自适应稀疏成像方法[J]. 电子与信息学报, 2016, 38(5): 1047-1054. doi: 10.11999/JEIT150884.

JIN Liangnian, SHEN Wenting, QIAN Yubin, et al. Adaptive sparse imaging approach for ultra-wideband through-the-wall radar in combined dictionaries[J]. Journal of Electronics Information Technology, 2016, 38(5): 1047-1054. doi: 10.11999/JEIT150884.

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