Multiple Model Rao-Blackwellized Particle Filter for Manoeuvring Target Tracking

Particle filters can become quite inefficient when applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, a novel multiple model Rao-Blackwellized particle filter (MMRBPF)-based algorithm has been proposed for manoeuvring target tracking in a cluttered environment. The advantage of the proposed approach is that the Rao-Blackwellization allows the algorithm to be partitioned into target tracking and model selection sub-problems, where the target tracking can be solved by the probabilistic data association filter, and the model selection by sequential importance sampling. The analytical relationship between target state and model is exploited to improve the efficiency and accuracy of the proposed algorithm. Moreover, to reduce the particle-degeneracy problem, the resampling approach is selectively carried out. Finally, experiment results, show that the proposed algorithm, has advantages over the conventional IMM-PDAF algorithm in terms of robust and efficiency.