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DISTRIBUTED GAUSSIAN PARTICLE FILTERING USING LIKELIHOOD CONSENSUS

Full Paper at IEEE Xplore

Distributed and Collaborative Signal Processing

Presented by: Ondrej Hlinka, Author(s): Ondrej Hlinka, Ondrej Sluciak, Franz Hlawatsch, Vienna University of Technology, Austria; Petar Djuric, Stony Brook University, United States; Markus Rupp, Vienna University of Technology, Austria

We propose a distributed implementation of the Gaussian particle filter (GPF) for use in a wireless sensor network. Each sensor runs a local GPF that computes a global state estimate. The updating of the particle weights at each sensor uses the joint likelihood function, which is calculated in a distributed way, using only local communications, via the recently proposed likelihood consensus scheme. A significant reduction of the number of particles can be achieved by means of another consensus algorithm. The performance of the proposed distributed GPF is demonstrated for a target tracking problem.

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