Online Estimation of Unknown Parameters in Multisensor-Multitarget Tracking: a Belief Propagation Approach

We propose a Bayesian multisensor-multitarget tracking framework, which adapts to randomly changing conditions by continually estimating unknown model parameters along with the target states. The time-evolution of the model parameters is described by a Markov chain and the parameters are incorporated in a factor graph that represents the statistical structure of the tracking problem. We then use the belief propagation (BP) message passing scheme to calculate the marginal posterior distributions of the targets and the model parameters in an efficient way that exploits conditional statistical independencies. As a concrete example, we develop an adaptive BP-based multisensor-multitarget tracking algorithm for maneuvering targets with multiple dynamic models and sensors with unknown and time-varying detection probabilities. The performance of the proposed algorithm is finally evaluated in a simulated scenario.