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

Yangsoo Kwon, Ram Mohan Narayanan, Muralidhar Rangaswamy

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Article number021007
JournalJournal of Electronic Imaging
Volume22
Issue number2
DOIs
StatePublished - Apr 1 2013

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imaging radar
Radar imaging
MIMO (control systems)
Target tracking
Ultra-wideband (UWB)
Mean square error
Antennas
Sampling
broadband
random sampling
norms
echoes
antennas
recovery
simulation

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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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.",
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Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging. / Kwon, Yangsoo; Narayanan, Ram Mohan; Rangaswamy, Muralidhar.

In: Journal of Electronic Imaging, Vol. 22, No. 2, 021007, 01.04.2013.

Research output: Contribution to journalArticle

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