[CAZ16] Source Position Estimation via Subspace based Joint Sparse Recovery

Conférence Internationale avec comité de lecture : 13th IEEE International Conference on Signa Processing (ICSP), November 2016, pp.1-5, Chengdu, China,

Auteurs: L. Chen , I. Ahriz , H. Zhang , H. Sun , D. le Ruyet

motcle:

Résumé: This paper addresses the multi-source localization problem by utilizing a novel subspace based joint sparse recovery approach. We firstly introduce a multiple measurement vector (MMV) positioning framework in the context of joint sparse recovery. To sufficiently degrade the effect of measurement noise at a lower cost of deployed anchors, we then optimally hybridize the compressive sensing and array signal processing so that a part of location supports are first identified by CS scheme and the remaining supports are determined by a generalized subspace criterion. Meanwhile, we design a post refined procedure to further improve the estimate accuracy by considering the grid assumption. Finally, a comprehensive set of simulations had been conducted to demonstrate the

Collaboration: Wuhan University

BibTeX

@inproceedings {
	CAZ16,
	title	=	"{Source Position Estimation via Subspace based Joint Sparse Recovery}",
	author	=	" L. CHEN and I. Ahriz and H. Zhang and H. Sun and D. le Ruyet ",
	booktitle	=	"{13th IEEE International Conference on Signa Processing (ICSP)}",
	year	=	2016,
	month	=	"November",
	pages	=	"1-5",
	address	=	"Chengdu, China",
}