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Proceedings Paper

Sparse radar imaging using 2D compressed sensing
Author(s): Qingkai Hou; Yang Liu; Zengping Chen; Shaoying Su
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Paper Abstract

Radar imaging is an ill-posed linear inverse problem and compressed sensing (CS) has been proved to have tremendous potential in this field. This paper surveys the theory of radar imaging and a conclusion is drawn that the processing of ISAR imaging can be denoted mathematically as a problem of 2D sparse decomposition. Based on CS, we propose a novel measuring strategy for ISAR imaging radar and utilize random sub-sampling in both range and azimuth dimensions, which will reduce the amount of sampling data tremendously. In order to handle 2D reconstructing problem, the ordinary solution is converting the 2D problem into 1D by Kronecker product, which will increase the size of dictionary and computational cost sharply. In this paper, we introduce the 2D-SL0 algorithm into the reconstruction of imaging. It is proved that 2D-SL0 can achieve equivalent result as other 1D reconstructing methods, but the computational complexity and memory usage is reduced significantly. Moreover, we will state the results of simulating experiments and prove the effectiveness and feasibility of our method.

Paper Details

Date Published: 7 October 2014
PDF: 7 pages
Proc. SPIE 9252, Millimetre Wave and Terahertz Sensors and Technology VII, 92520T (7 October 2014); doi: 10.1117/12.2067223
Show Author Affiliations
Qingkai Hou, National Univ. of Defense Technology (China)
Yang Liu, National Univ. of Defense Technology (China)
Zengping Chen, National Univ. of Defense Technology (Chile)
Shaoying Su, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 9252:
Millimetre Wave and Terahertz Sensors and Technology VII
Neil Anthony Salmon; Eddie L. Jacobs, Editor(s)

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