Distributed Estimation using Bayesian Consensus Filtering
Abstract
We present the Bayesian consensus filter (BCF) for tracking a moving target using a networked group of sensing agents and achieving consensus on the best estimate of the probability distributions of the targets states. Our BCF framework can incorporate nonlinear target dynamic models, heterogeneous nonlinear measurement models, non-Gaussian uncertainties, and higher-order moments of the locally estimated posterior probability distribution of the targets states obtained using Bayesian filters. If the agents combine their estimated posterior probability distributions using a logarithmic opinion pool, then the sum of KullbackLeibler divergences between the consensual probability distribution and the local posterior probability distributions is minimized. Rigorous stability and convergence results for the proposed BCF algorithm with single or multiple consensus loops are presented. Communication of probability distributions and computational methods for implementing the BCF algorithm are discussed along with a numerical example.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 06, 2014
- Accession Number
- AD1015563
Entities
People
- Saptarshi Bandyopadhyay
- Soon-Jo Chung
Organizations
- University of Illinois Urbana–Champaign