Particle filter adaptation for distributed sensors via set membership

A distributed set-membership-constrained particle filter (SMCPF) is developed for decentralized tracking applications using wireless sensor networks. Unlike existing PF alternatives, SMC-PF offers reduced overhead for inter-sensor communications because it requires only particle weights to be exchanged among sensors, instead of raw measurements or parameters of a Gaussian mixture model. SMC-PF relies on a novel distributed adaptation scheme based on successive set intersections that can afford reduced number of particles without sacrificing performance. Conditions are provided to quantify the variance reduction of the SMC-PF-based state estimator. Simulations corroborate the ability of the SMCPF to considerably outperform the bootstrap PF for a fixed number of particles.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Shahrokh Farahmand, Stergios I. Roumeliotis, Georgios B. Giannakis
• Keywords: Distributed Set-membership-constrained Particle, Gaussian Mixture Model, ICASSP 2010, Signal Processing, Wireless Sensor Networks