Near-optimal Bayesian localization via incoherence and sparsity

This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal solution. By exploiting the spatial sparsit...

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Bibliographic Details
Published in:	2009 International Conference on Information Processing in Sensor Networks pp. 205 - 216
Main Authors:	Cevher, Volkan, Boufounos, Petros, Baraniuk, Richard G., Gilbert, Anna C., Strauss, Martin J.
Format:	Conference Proceeding
Language:	English
Published:	Washington, DC, USA IEEE Computer Society 13.04.2009 IEEE
Series:	ACM Conferences
Subjects:	Approximation algorithms Bandwidth Bayesian methods bearing estimation Computer networks Costs Direction of arrival estimation Distributed computing General and reference > Cross-computing tools and techniques > Design Hardware > Communication hardware, interfaces and storage localization Mathematics of computing > Mathematical analysis > Functional analysis > Approximation Networks Networks > Network services > Network management Permission Position measurement sensor networks Sparse approximation spatial sparsity Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis Timing
ISBN:	1424451086, 9781424451081
Online Access:	Get full text
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