Improved distributed particle filters for tracking in a wireless sensor network

Abstract

A novel distributed particle filter algorithm is presented, called drift homotopy likelihood bridging particle filter (DHLB-PF). The DHLB-PF is designed to surmount the degeneracy problem by employing a multilevel Markov chain Monte Carlo (MCMC) procedure after the resampling step of particle filtering. DHLB-PF considers a sequence of pertinent stationary distributions which facilitates the MCMC step as well as explores the state space with a higher degree of freedom. The proposed algorithm is tested in a multi-target tracking problem using a wireless sensor network where no fusion center is required for data processing. The observations are gathered only from the informative sensors, which are sensing useful observations of the nearby moving targets. The detection of those informative sensors, which are typically a small portion of the sensor network, is taking place by using a sparsity-aware matrix decomposition technique. Simulation results showcase that the DHLB-PF outperforms current popular tracking algorithms.