Anytime optimal distributed Kalman filtering and smoothing

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

doi:10.1109/SSP.2007.4301282

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 proceeding › Conference contribution

9 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 language	English (US)
Title of host publication	2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages	368-372
Number of pages	5
DOIs	https://doi.org/10.1109/SSP.2007.4301282
State	Published - Dec 1 2007
Event	2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States Duration: Aug 26 2007 → Aug 29 2007

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings

Other

Other	2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
Country/Territory	United States
City	Madison, WI
Period	8/26/07 → 8/29/07

Keywords

Distributed estimation and tracking
Kalman filtering

Publisher link

10.1109/SSP.2007.4301282

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Anytime optimal distributed Kalman filtering and smoothing. / Schizas, Ioannis D.; Giannakis, Georgios B.; Roumeliotis, Stergios I. et al.
2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings. 2007. p. 368-372 4301282 (IEEE Workshop on Statistical Signal Processing Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Schizas, ID, Giannakis, GB , Roumeliotis, SI & Ribeiro, A 2007, Anytime optimal distributed Kalman filtering and smoothing. in 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings., 4301282, IEEE Workshop on Statistical Signal Processing Proceedings, pp. 368-372, 2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007, Madison, WI, United States, 8/26/07. https://doi.org/10.1109/SSP.2007.4301282

@inproceedings{14f7727529bb42c8b0497089504f98de,

title = "Anytime optimal distributed Kalman filtering and smoothing",

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.",

keywords = "Distributed estimation and tracking, Kalman filtering",

author = "Schizas, {Ioannis D.} and Giannakis, {Georgios B.} and Roumeliotis, {Stergios I.} and Alejandro Ribeiro",

year = "2007",

month = dec,

day = "1",

doi = "10.1109/SSP.2007.4301282",

language = "English (US)",

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series = "IEEE Workshop on Statistical Signal Processing Proceedings",

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T1 - Anytime optimal distributed Kalman filtering and smoothing

AU - Schizas, Ioannis D.

AU - Giannakis, Georgios B.

AU - Roumeliotis, Stergios I.

AU - Ribeiro, Alejandro

PY - 2007/12/1

Y1 - 2007/12/1

N2 - 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.

AB - 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.

KW - Distributed estimation and tracking

KW - Kalman filtering

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BT - 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings

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ER -

Anytime optimal distributed Kalman filtering and smoothing

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