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

Yangsoo Kwon; Ram M. Narayanan; Muralidhar Rangaswamy

doi:10.1117/1.JEI.22.2.021008

5 February 2013 Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging

Yangsoo Kwon, Ram M. Narayanan, Muralidhar Rangaswamy

Author Affiliations +

Funded by: Air Force Office of Scientific Research (AFOSR)

Journal of Electronic Imaging, Vol. 22, Issue 2, 021008 (February 2013). https://doi.org/10.1117/1.JEI.22.2.021008

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.

Citation Download Citation

Yangsoo Kwon, Ram M. Narayanan, and Muralidhar Rangaswamy "Target detection and reconstruction for compressive multiple-input, multiple-output ultra-wideband noise radar imaging," Journal of Electronic Imaging 22(2), 021008 (5 February 2013). https://doi.org/10.1117/1.JEI.22.2.021008

Published: 5 February 2013

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE