Anytime optimal distributed Kalman filtering and smoothing

Ioannis D. Schizas, Georgios B. Giannakis, Stergios I. Roumeliotis, Alejandro Ribeiro

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

12 Scopus citations

Abstract

Distributed algorithms are derived for estimation and smoothing of nonstationary dynamical processes based on correlated observations collected by ad hoc wireless sensor networks (WSNs). Specifically, distributed Kalman filtering (KF) and smoothing schemes are constructed for any-time minimum mean-square error (MMSE) optimal consensus-based state estimation using WSNs. The novel distributed filtering/smoothing approach is flexible to trade-off estimation delay for MSE reduction, while it exhibits robustness in the presence of communication noise. Numerical examples demonstrate the merits of the proposed approach with respect to existing alternatives.

Original languageEnglish (US)
Title of host publication2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages368-372
Number of pages5
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States
Duration: Aug 26 2007Aug 29 2007

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
Country/TerritoryUnited States
CityMadison, WI
Period8/26/078/29/07

Keywords

  • Distributed estimation and tracking
  • Kalman filtering

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