Special Section on Compressive Sensing for Imaging

Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging

[+] Author Affiliations
Yangsoo Kwon

The Pennsylvania State University, Department of Electrical Engineering, PA 16802

Ram M. Narayanan

The Pennsylvania State University, Department of Electrical Engineering, PA 16802

Muralidhar Rangaswamy

Air Force Research Laboratory, Code RYAP, Building 620, 2241 Avionics Circle, Wright-Patterson Air Force Base, OH 45433-7132

J. Electron. Imaging. 22(2), 021008 (Feb 05, 2013). doi:10.1117/1.JEI.22.2.021008
History: Received August 25, 2012; Revised November 7, 2012; Accepted January 21, 2013
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Abstract.  We propose a sample selection method for multiple-input, multiple-output ultra-wideband noise radar imaging using compressive sensing. The proposed sample selection is based on comparing the norm values of candidates among the potential received signal and selecting the largest M samples among N per antenna to obtain selection diversity. Moreover, we propose an adaptive weighting allocation that improves reconstruction accuracy of compressive sensing by maximizing the mutual information between target echoes and transmitted signals. This weighting scheme is applicable to both sample selection schemes, a conventional random sampling and the proposed selection. Further, the weighting allocation with the knowledge of recovery error is proposed for more practical scenarios. Simulations show that the proposed selection and weighting allocation enhance multiple target detection probability and reduce normalized mean square error.

© 2013 SPIE and IS&T

Citation

Yangsoo Kwon ; Ram M. Narayanan and Muralidhar Rangaswamy
"Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging", J. Electron. Imaging. 22(2), 021008 (Feb 05, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.2.021008


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