Paper Info
Title
Global methods for compressive sensing in MIMO radar with distributed sensors.
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-andbound 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 under-sampled 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.
Year
DOI
Venue
2011
10.1109/ACSSC.2011.6190269
ACSCC
Keywords
DocType
ISSN
MIMO radar,compressed sensing,concave programming,distributed sensors,radar signal processing,target tracking,time-domain analysis,tree searching,active sensors,branch-and-bound type algorithm,compressive sensing,distributed sensors,global methods,global solution,nonconvex localization problem,nonconvex optimization,receive elements,resolution target localization,signal measurements,sparse representation,spatial domain,time domain,transmit elements,undersampled MIMO radar
Conference
1058-6393
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Marco Rossi1262.34
Alexander M. Haimovich261869.28
Y. C. Eldar36399458.37