Distributed Estimation using Bayesian Consensus Filtering
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 target's 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 target's states obtained using Bayesian filters. If the agents combine their estimated posterior probability distributions using a logarithmic opinion pool, then the sum of Kullback–Leibler 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.
© 2014 AACC. Date Added to IEEE Xplore: 21 July 2014. This research was supported by AFOSR grant FA95501210193. An extended version of this paper has been submitted to IEEE Transactions on Automatic Control. (http://arxiv.org/abs/1403.3117) Last revised 13 Oct 2016. The authors would like to thank F. Hadaegh, D. Bayard, S. Hutchinson, P. Voulgaris, M. Egerstedt, A. Gupta, A. Dani, D. Morgan, S. Sengupta, and A. Olshevsky for stimulating discussions about this paper.
Submitted - 1403.3117.pdf