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Proceedings Paper

Multi-static passive SAR imaging based on Bayesian compressive sensing
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Paper Abstract

Passive radar systems, which utilize broadcast and navigation signals as sources of opportunity, have attracted significant interests in recent years due to their low cost, covertness, and the availability of different illuminator sources. In this paper, we propose a novel method for synthetic aperture imaging in multi-static passive radar systems based on a group sparse Bayesian learning technique. In particular, the problem of imaging sparse targets is formulated as a group sparse signal reconstruction problem, which is solved using a complex multi- task Bayesian compressive sensing (CMT-BCS) method to achieve a high resolution. The proposed approach significantly improves the imaging resolution beyond the range resolution. Compared to the other group sparse signal reconstruction methods, such as the block orthogonal matching pursuit (BOMP) and group Lasso, the CMT-BCS provides significant performance improvement for the reconstruction of sparse targets in the redundant dictionary with high coherence. Simulations results demonstrate the superior performance of the proposed approach.

Paper Details

Date Published: 23 May 2014
PDF: 9 pages
Proc. SPIE 9109, Compressive Sensing III, 910902 (23 May 2014); doi: 10.1117/12.2050524
Show Author Affiliations
Qisong Wu, Villanova Univ. (United States)
Yimin D. Zhang, Villanova Univ. (United States)
Moeness G. Amin, Villanova Univ. (United States)
Braham Himed, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9109:
Compressive Sensing III
Fauzia Ahmad, Editor(s)

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