Distributed iteratively quantized Kalman filtering for wireless sensor networks

Erie J. Msechu, Stergios Roumeliotis, Alejandro Ribeiro, Georgios B Giannakis

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

5 Scopus citations

Abstract

Estimation and tracking of generally nonstationary Markov processes is of paramount importance for applications such as localization and navigation. In this context, ad hoc wireless sensor networks (WSNs) offer distributed Kalman filtering (KF) based algorithms with documented merits over centralized alternatives. Adhering to the limited power and bandwidth resources WSNs must operate with, this paper introduces a novel distributed KF estimator based on quantized measurement innovations. The quantized observations and the distributed nature of the iteratively quantized KF algorithm are amenable to the resource constraints of the ad hoc WSNs. Analysis and simulations show that KF-like tracking based on m bits of iteratively quantized innovations communicated among sensors exhibits MSE performance identical to a KF based on analog-amplitude observations applied to an observation model with noise variance increased by a factor of [1 - (1-2/π)m]-1. With minimal communication overhead, the mean square error (MSE) of the distributed KF-like tracker based on 2-3 bits is almost indistinguishable from that of the clairvoyant KF.

Original languageEnglish (US)
Title of host publicationConference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages646-650
Number of pages5
DOIs
StatePublished - Dec 1 2007
Event41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States
Duration: Nov 4 2007Nov 7 2007

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/4/0711/7/07

Keywords

  • Distributed state estimation
  • Kalman filtering
  • Limited-rate communication
  • Quantized observations
  • Wireless sensor networks

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