Time-space-sequential distributed particle filtering with low-rate communications

Authors:
Ondrej Hlinka;Petar M. Djurić;Franz Hlawatsch
Affiliations:
Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology;Department of Electrical and Computer Engineering, Stony Brook University;Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

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Abstract

We present a distributed particle filtering scheme for time-space-sequential Bayesian state estimation in wireless sensor networks. Low-rate inter-sensor communications between neighboring sensors are achieved by transmitting Gaussian mixture (GM) representations instead of particles. The GM representations are calculated using a clustering algorithm. We also propose a "look-ahead" technique for designing the proposal density used for importance sampling. Simulation results for a target tracking application demonstrate the performance of our distributed particle filter and, specifically, the advantage of the look-ahead proposal design over a conventional design.