利用塊間耦合稀疏貝葉斯學(xué)習(xí)的建筑物布局成像方法
doi: 10.11999/JEIT170719 cstr: 32379.14.JEIT170719
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2.
(桂林電子科技大學(xué)信息與通信學(xué)院 桂林 541004)
基金項目:
國家自然科學(xué)基金(61461012)，廣西自然科學(xué)基金(2017GXNSFAA198050)，廣西無線寬帶通信與信號處理重點實驗室2016主任基金(GXKL06160106)
Building Layout Imaging Method Using the Inter-block Coupling Sparse Bayesian Learning
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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)
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摘要: 該文針對現(xiàn)有穿墻雷達建筑物布局成像中擴展目標(biāo)稀疏成像方法未能有效利用墻體反射信號的結(jié)構(gòu)稀疏性，導(dǎo)致成像中出現(xiàn)墻體不連貫和墻體輪廓不明顯的問題，提出一種利用稀疏信號塊間耦合的建筑物布局成像方法。該方法在塊稀疏信號特性的高斯分層先驗?zāi)Ｐ偷幕A(chǔ)上，利用塊間耦合系數(shù)進一步表征場景中墻體反射信號的結(jié)構(gòu)稀疏性，然后將其引入到控制稀疏信號先驗概率分布的超參數(shù)中，從而把稀疏信號的結(jié)構(gòu)性轉(zhuǎn)化為超參數(shù)的耦合關(guān)系，最后利用期望最大化(EM)算法求解超參數(shù)的最大后驗(MAP)估計。仿真和實驗數(shù)據(jù)處理結(jié)果表明，該方法有效改善了墻體的成像質(zhì)量。
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關(guān)鍵詞:
- 穿墻雷達 /
- 建筑物布局成像 /
- 結(jié)構(gòu)稀疏性 /
- 稀疏貝葉斯學(xué)習(xí) /
- 塊間耦合
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. -
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. 張燕, 晉良念. 結(jié)合TV約束的穿墻雷達擴展目標(biāo)成像方法[J]. 雷達科學(xué)與技術(shù), 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. 晉良念, 申文婷, 錢玉彬, 等. 組合字典下超寬帶穿墻雷達自適應(yīng)稀疏成像方法[J]. 電子與信息學(xué)報, 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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