Distributed Kalman filtering based on severely quantized WSN data

Alejandro Ribeiro, Georgios B. Giannakis

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

1 Scopus citations

Abstract

This paper deals with recursive random parameter or state estimation for use in distributed tracking applications implemented with a Wireless Sensor Network (WSN). Bandwidth and energy limitations encountered with WSNs, motivate quantization of individual sensor observations before their digital transmission to the fusion center, where tracking is to be performed. Recent results investigating the intertwining between quantization and batch parameter estimation with WSNs, hint that quantization to a single bit per sensor may lead to a small penalty in state estimation variance. Relying on a dynamical model, we derive a Kalman-like Filter (KF) based on what we term "sign- differential" quantization, and establish that for all cases of practical interest, its asymptotic variance comes surprisingly close to the asymptotic variance of the clairvoyant minimum mean-square error KF state estimator which is based on the original (analog) observations. In a nutshell, this paper establishes the rather unexpected result that tracking with a WSN can simply rely on sensor observations quantized to a single bit.

Original languageEnglish (US)
Title of host publication2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
PublisherIEEE Computer Society
Pages1250-1255
Number of pages6
ISBN (Print)0780394046, 9780780394049
DOIs
StatePublished - Jan 1 2005
Event2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Bordeaux, France
Duration: Jul 17 2005Jul 20 2005

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2005

Other

Other2005 IEEE/SP 13th Workshop on Statistical Signal Processing
CountryFrance
CityBordeaux
Period7/17/057/20/05

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