組合字典下超寬帶穿墻雷達(dá)自適應(yīng)稀疏成像方法
doi: 10.11999/JEIT150884
基金項(xiàng)目:
國家自然科學(xué)基金(61461012)，廣西區(qū)自然科學(xué)基金(2013GXNSFAA019329, 2013GXNSFAA019004)，認(rèn)知無線電與信息處理教育部重點(diǎn)實(shí)驗(yàn)室2015主任基金項(xiàng)目(CRKL150107)
Adaptive Sparse Imaging Approach for Ultra-wideband Through-the-wall Radar in Combined Dictionaries
Funds:
The National Natural Science Foundation of China (61461012), Guangxi Natural Science Foundation (2013GXNSFAA019329, 2013GXNSFAA019004), Cognitive Radio and the Ministry of Education Key Laboratory of Information Processing, 2015 the Fund Project of director (CRKL150107)
-
摘要: 針對現(xiàn)有超寬帶穿墻雷達(dá)稀疏成像算法大多只采用點(diǎn)目標(biāo)稀疏基表示模型和稀疏優(yōu)化的正則化參數(shù)不能被自適應(yīng)調(diào)整以及目標(biāo)位置不在劃分網(wǎng)格上帶來虛假像的問題，該文提出一種基于貝葉斯證據(jù)框架的自適應(yīng)稀疏成像方法。該方法首先利用組合字典獨(dú)立稀疏表示場景中的點(diǎn)目標(biāo)和擴(kuò)展目標(biāo)，然后在建立的偏離網(wǎng)格稀疏表示模型的基礎(chǔ)上分層最大化各參數(shù)的似然函數(shù)，用第1層推理結(jié)合共軛梯度算法估計(jì)組合字典的各稀疏表示系數(shù)，用第2層推理估計(jì)正則化參數(shù)和目標(biāo)的偏離網(wǎng)格量，最終通過迭代優(yōu)化參數(shù)的設(shè)置得到問題的求解。仿真和實(shí)驗(yàn)結(jié)果表明，該方法不僅同時(shí)自適應(yīng)增強(qiáng)穿墻場景中的點(diǎn)目標(biāo)和擴(kuò)展目標(biāo)，還消除了偏離網(wǎng)格目標(biāo)引起的虛假像。
-
關(guān)鍵詞:
- 超寬帶穿墻雷達(dá)稀疏成像 /
- 組合字典 /
- 證據(jù)框架 /
- 參數(shù)自適應(yīng)調(diào)整
Abstract: The existing algorithms of ultra-wideband through-the-wall radar sparse imaging mostly adopt point target model. Also the regularization parameter of sparse optimization can not be adjusted adaptively, and the ghost imaging can be produced if the targets are not positioned at the pre-discretized grid location. To deal with the above issues, an adaptive sparse imaging algorithm based on Bayesian evidence framework is proposed, which represents sparsely the scene with the point targets and the extended targets by combination of appropriate dictionaries, and maximizes hierarchically the likelihood?function of all parameters as well. The first-level inference of the Bayesian, combined with conjugate gradient algorithm, is adopted to estimate the sparse representation coefficients of the combined dictionaries. The second-level inference of the Bayesian is adopted to estimate the regularization parameter as well as the targets off-grid shifts. Therefore, the problem can be solved through iterative optimizating the parameter setting. The simulation and experimental results show that the proposed method can not only adaptively enhance the characteristics of both the point targets and the extended targets, but also mitigate ghosts caused by off-grid targets. -
LI G and BURKHOLDER R J. Hybrid matching pursuit for distributed through-wall radar imaging[J]. IEEE Transactions on Antennas and Propagation, 2015, 63(4): 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. 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. AMIN M G and AHMAD F. Change detection analysis of human moving behind walls[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(3): 1410-1425. WU Qisong, ZHANG Y D, AHMAD F, et al. Compressive sensing based high-resolution polarimetic through-the-wall radar imaging exploiting taget characteristics[J]. IEEE Antennas and Wireless Progagation Letters, 2014, 99: 1-4. doi: 10.1109/LAWP.2014.238087. XIA Shugao and LIU Fengshan. Off-gird compressive sensing through-the-wall radar imaging[J]. Proceedings of SPIE, 2014, 9077: 90771F-1-8. 晉良念, 錢玉彬, 劉慶華, 等. 超寬帶穿墻雷達(dá)偏離網(wǎng)格目標(biāo)稀疏成像方法[J]. 儀器儀表學(xué)報(bào), 2015, 36(4): 743-748. JIN Liangnian, QIAN Yubin, LIU Qinghua, et al. Off-grid target sparse imaging method for ultra-wideband though- the-wall rader[J]. Chinese Journal of Scientific Instrument, 2015, 36(4): 743-748. BROWNE K E, BURKHOLDER R J, and VOLAKIS J L. Fast optimization of through-wall radar images via the method of lagrange multipliers[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(1): 320-328. doi: 10.1109/TAP.2012.2220321. SAMADI S,ETIN M, and MASNADI-SHIRAZI M A. Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 821-825. doi: 10.1109/LGRS. 2012.2225016. LIU Hongchao, JIU Bo, LIU Hongwei, et al. An adaptive ISAR imaging method based on evidence framework[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6): 1031-1035. doi: 10.1109/LGRS.2013.2281194. JIN T, CHEN B, and ZHOU Z. Imaging-domain estimation of wall parameters for autofocusing of through-the-wall SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1836-1843. doi: 10.1109/TGRS.2012. 2206395. LAGUNAS E, AMIN M G, AHMAD F, et al. Joint wall mitigation and compressive sensing for indoor image reconstruction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 891-906. doi: 10.1109/TGRS. 2012.2203824. PANT J and KRISHNAN S. Reconstruction of ECG signals for compressive sensing by promoting sparsity on the gradient[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, 2013: 993-997. 安芹力, 馮有前, 高大化, 等. 組合正交基字典稀疏分解快速匹配追蹤算法[J]. 電子設(shè)計(jì)工程, 2011, 19(2): 78-80. AN Qinli, FENG Youqian, GAO Dahua, et al. A quick MP algorithm of sparse decomposition by overcomplete dictionary combined orthogonal bases[J]. Electronic Design Engineering, 2011, 19(2): 78-80. SHENG F and JIAO D. A deterministic-solution based fast eigenvalue solver with guaranteed convergence for finite-element based 3-D electromagnetic analysis[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(7): 3701-3711 doi: 10.1109/TAP.2013.2258315. -1711. doi: 10.1109/TAP.2015.2398115. -
計(jì)量
- 文章訪問數(shù): 1228
- HTML全文瀏覽量: 167
- PDF下載量: 295
- 被引次數(shù): 0
下載:
