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Journal of Electronic Imaging

Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging
Author(s): Yangsoo Kwon; Ram M. Narayanan; Muralidhar Rangaswamy
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Paper 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.

Paper Details

Date Published: 5 February 2013
PDF: 16 pages
J. Electron. Imaging. 22(2) 021008 doi: 10.1117/1.JEI.22.2.021008
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Yangsoo Kwon, The Pennsylvania State Univ. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)
Muralidhar Rangaswamy, Air Force Research Lab. (United States)


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