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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Veröffentlicht in:2009 International Conference on Information Processing in Sensor Networks S. 205 - 216
Hauptverfasser: Cevher, Volkan, Boufounos, Petros, Baraniuk, Richard G., Gilbert, Anna C., Strauss, Martin J.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 13.04.2009
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
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-Zugang:Volltext
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Beschreibung
Zusammenfassung: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 sparsity of the posterior density, we demonstrate that the optimal solution can be computed using fast sparse approximation algorithms. We show that exploiting the signal sparsity can reduce the sensing and computational cost on the sensors, as well as the communication bandwidth. We further illustrate that the sparsity of the source locations can be exploited to decentralize the computation of the source locations and reduce the sensor communications even further. We also discuss how recent results in 1-bit compressive sensing can significantly reduce the amount of inter-sensor communications by transmitting only the intrinsic timing information. Finally, we develop a computationally efficient algorithm for bearing estimation using a network of sensors with provable guarantees.
ISBN:1424451086
9781424451081
DOI:10.5555/1602165.1602185