Scalable Data Association and Multi-Target Tracking Under a Poisson Mixture Measurement Process
Qing Li, Jiaming Liang, Simon Godsill
Abstract
Measurement rates for both targets and clutter have been assumed to be known a priori in most existing tracking systems, whereas practically the rates may be unknown to users or time-varying. This paper therefore fills this gap by developing a Poisson mixture process tracker (PMPT) to capture the temporal characteristics of the rate parameters under a Poisson mixture measurement process. Specifically, the Generalized inverse Gaussian (GIG) distribution is proposed as a prior for Poisson rates, and two novel priors, the time independent GIG prior and the GIG Markov chain prior, are designed. In addition, a scalable inference framework is introduced to enable efficient data association and parallel updating of target states under a sequential Markov chain Monte Carlo (MCMC) scheme with linear complexity in the number of measurements and targets. Results show that our proposed method can provide a robust solution in highly dynamic detection probability environments.