Multiple model Rao-Blackwellized particle filter

Abstract

In this paper, we proposed a new multiple model Rao-Blackwellized particle filter (MMRBPF) based algorithm for maneuvering target tracking. 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. Finally, the experiment results show that the proposed algorithm results in more accurate tracking than the existing one.