Distributed Gaussian particle filtering using likelihood consensus

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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Ondrej Hlinka, Ondrej Sluciak, Franz Hlawatsch, Petar M. Djuric, Markus Rupp
• Keywords: Gaussian Particle filter, ICASSP 2011, Sensor, Signal Processing, Wireless Sensor Network