Optimal quantizers for distributed Bayesian estimation

Aditya Vempaty, Biao Chen, Pramod K. Varshney

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

4 Scopus citations

Abstract

In this paper, we consider the problem of quantizer design for distributed estimation under the Bayesian criterion. We derive general optimality conditions under the assumption of conditionally independent observations at the local sensors and show that for a conditionally unbiased and efficient estimator at the Fusion Center, identical quantizers are optimal when local observations have identical distributions. This results in an N-fold reduction in complexity where N is the number of sensors. We illustrate our approach by applying it to the location parameter estimation problem.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages4893-4897
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Distributed Estimation
  • Posterior Cramér Rao Lower Bound (PCRLB)
  • Quantizer Design

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Vempaty, A., Chen, B., & Varshney, P. K. (2013). Optimal quantizers for distributed Bayesian estimation. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 4893-4897). [6638591] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638591