Global methods for compressive sensing in MIMO radar with distributed sensors

Document Type

Conference Proceeding

Publication Date

12-1-2011

Abstract

We study compressive sensing methods for target localization in MIMO radar. While much attention has been given to compressive sensing of signal measurements in the time domain, this work focuses on the spatial domain. We propose a framework in which the target localization with distributed, active sensors is formulated as a nonconvex optimization. By leveraging a sparse representation, we devise a branch-and-bound type algorithm that provides a global solution to the nonconvex localization problem. It is shown that this method can achieve high resolution target localization with a highly undersampled MIMO radar with transmit/receive elements placed at random. A lower bound is developed on the number of required transmit/receive elements required to ensure accurate target localization with high probability. © 2011 IEEE.

Identifier

84861325783 (Scopus)

ISBN

[9781467303231]

Publication Title

Conference Record Asilomar Conference on Signals Systems and Computers

External Full Text Location

https://doi.org/10.1109/ACSSC.2011.6190269

ISSN

10586393

First Page

1506

Last Page

1510

This document is currently not available here.

Share

COinS