Radar Change Imaging With Undersampled Data Based on Matrix Completion and Bayesian Compressive Sensing

Matrix completion (MC) is a technique of reconstructing a low-rank matrix from a subset of matrix elements. This letter proposes an approach for change imaging from undersampled stepped-frequency-radar data via MC. We demonstrate that MC can be used to reconstruct the unknown samples. Based on the recovered full sample data, we then perform the estimation of the change image using a Bayesian compressive sensing (BCS) approach. Compared with existing compressive sensing (CS)-based techniques, which are sensitive to noise and clutter, the proposed method reduces the false-alarm rate and achieves sparser change imaging, which is due to more available data offered by MC and our explicit consideration of clutter and additive noise in the imaging procedure. The effectiveness of the proposed method is validated with experimental results based on raw radar data.

[1]  Christopher M. Kreucher,et al.  A Compressive Sensing Approach to Multistatic Radar Change Imaging , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Y. Pi,et al.  Bayesian compressive sensing in synthetic aperture radar imaging , 2012 .

[3]  Shengqi Zhu,et al.  Wideswath synthetic aperture radar ground moving targets indication with low data rate based on compressed sensing , 2013 .

[4]  Emmanuel J. Candès,et al.  Matrix Completion With Noise , 2009, Proceedings of the IEEE.

[5]  Biao Hou,et al.  Novel Change Detection in SAR Imagery Using Local Connectivity , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..

[7]  Lawrence Carin,et al.  Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.

[8]  Wen Hong,et al.  Efficient l q regularisation algorithm with range-azimuth decoupled for SAR imaging , 2014 .

[9]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[10]  Victoria Stodden,et al.  Breakdown Point of Model Selection When the Number of Variables Exceeds the Number of Observations , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[11]  Jorma Laaksonen,et al.  Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Lars M. H. Ulander,et al.  Synthetic-aperture radar processing using fast factorized back-projection , 2003 .

[13]  Uwe Stilla,et al.  Comparative study of change detection for reconstruction monitoring based on very high resolution optical data , 2011, 2011 Joint Urban Remote Sensing Event.

[14]  J. Thirion,et al.  Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences , 1999, IEEE Transactions on Medical Imaging.