Distributed Estimation using Bayesian Consensus Filtering

Understand

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.