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International Journal of Antennas and Propagation
Volume 2013, Article ID 142602, 16 pages
Research Article

Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch

1Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada M5B 2K3
2Department of Electrical Engineering, COMSATS Institute of IT, Wah Campus, Wah 47040, Pakistan

Received 4 April 2013; Revised 4 October 2013; Accepted 6 October 2013

Academic Editor: Krzysztof Kulpa

Copyright © 2013 Ahmed Shaharyar Khwaja et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We present compressed sensing (CS) synthetic aperture radar (SAR) moving target imaging in the presence of dictionary mismatch. Unlike existing work on CS SAR moving target imaging, we analyze the sensitivity of the imaging process to the mismatch and present an iterative scheme to cope with dictionary mismatch. We analyze and investigate the effects of mismatch in range and azimuth positions, as well as range velocity. The analysis reveals that the reconstruction error increases with the mismatch and range velocity mismatch is the major cause of error. Instead of using traditional Laplacian prior (LP), we use Gaussian-Bernoulli prior (GBP) for CS SAR imaging mismatch. The results show that the performance of GBP is much better than LP. We also provide the Cramer-Rao Bounds (CRB) that demonstrate theoretically the lowering of mean square error between actual and reconstructed result by using the GBP. We show that a combination of an upsampled dictionary and the GBP for reconstruction can deal with position mismatch effectively. We further present an iterative scheme to deal with the range velocity mismatch. Numerical and simulation examples demonstrate the accuracy of the analysis as well as the effectiveness of the proposed upsampling and iterative scheme.