Approximate distributed Kalman filtering in sensor networks with quantifiable performance

Authors:
Demetri P. Spanos;Reza Olfati-Saber;Richard M. Murray
Affiliations:
California Institute of Technology;University of California, Los Angeles;California Institute of Technology
Venue:
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Year:
2005

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Abstract

We analyze the performance of an approximate distributed Kalman filter proposed in recent work on distributed coordination. This approach to distributed estimation is novel in that it admits a systematic analysis of its performance as various network quantities such as connection density, topology, and bandwidth are varied. Our main contribution is a frequency-domain characterization of the distributed estimator's steady-state performance; this is quantified in terms of a special matrix associated with the connection topology called the graph Laplacian, and also the rate of message exchange between immediate neighbors in the communication network.