  We present a consensus-based distributed particle filter (PF) for wireless
sensor networks. Each sensor runs a local PF to compute a global state estimate
that takes into account the measurements of all sensors. The local PFs use the
joint (all-sensors) likelihood function, which is calculated in a distributed
way by a novel generalization of the likelihood consensus scheme. A performance
improvement (or a reduction of the required number of particles) is achieved by
a novel distributed, consensus-based method for adapting the proposal densities
of the local PFs. The performance of the proposed distributed PF is
demonstrated for a target tracking problem.
