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We present a consensus-based distributed particle filter (PF) for wirelesssensor networks. Each sensor runs a local PF to compute a global state estimatethat takes into account the measurements of all sensors. The local PFs use thejoint (all-sensors) likelihood function, which is calculated in a distributedway by a novel generalization of the likelihood consensus scheme. A performanceimprovement (or a reduction of the required number of particles) is achieved bya novel distributed, consensus-based method for adapting the proposal densitiesof the local PFs. The performance of the proposed distributed PF isdemonstrated for a target tracking problem.

Likelihood Consensus-Based Distributed Particle Filtering with Distributed Proposal Density Adaptation