Anytime Optimal Distributed Kalman Filtering and Smoothing

Authors:
Ioannis D. Schizas;Georgios B. Giannakis;Stergios I. Roumeliotis;Alejandro Ribeiro
Affiliations:
University of Minnesota, 200 Union Str. SE, Minneapolis, MN 55455, USA;University of Minnesota, 200 Union Str. SE, Minneapolis, MN 55455, USA;University of Minnesota, 200 Union Str. SE, Minneapolis, MN 55455, USA;University of Minnesota, 200 Union Str. SE, Minneapolis, MN 55455, USA
Venue:
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Year:
2007

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Diffusion distributed Kalman filtering with adaptive weights

Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers

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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.