Near-optimal Bayesian localization via incoherence and sparsity

Volkan Cevher, Petros Boufounos, Richard G. Baraniuk, Anna C. Gilbert, Martin J. Strauss

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2009 International Conference on Information Processing in Sensor Networks, IPSN 2009
Pages205-216
Number of pages12
StatePublished - Nov 16 2009
Event2009 International Conference on Information Processing in Sensor Networks, IPSN 2009 - San Francisco, CA, United States
Duration: Apr 13 2009Apr 16 2009

Other

Other2009 International Conference on Information Processing in Sensor Networks, IPSN 2009
CountryUnited States
CitySan Francisco, CA
Period4/13/094/16/09

Keywords

  • Bearing estimation
  • Localization
  • Sensor networks
  • Sparse approximation
  • Spatial sparsity

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Electrical and Electronic Engineering

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