Adaptive Sparse Imaging Approach for Ultra-wideband Through-the-wall Radar in Combined Dictionaries
JIN Liangnian①② SHEN Wenting② QIAN Yubin② OUYANG Shan②
①(Guangxi Key Laboratory of Wireless Wideband Communication & Signal Processing, Guilin 541004, China) ②(Institute of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China)
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):
17
01-1711. doi: 10.1109/TAP.2015.2398115.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
XIA Shugao and LIU Fengshan. Off-gird compressive sensing through-the-wall radar imaging[J]. Proceedings of SPIE, 2014, 9077: 90771F-1-8.
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.
[8]
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.
[9]
SAMADI S,ÇETIN 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.
[10]
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.
[11]
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.
[12]
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.
[13]
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.
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.
[15]
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.
