Target tracking based on particle filtering in passive sensor array

In this paper, a new Multiple Model Rao-Blackwellized Particle Filter (MMRBPF) based algorithm is proposed for maneuvering target tracking in passive sensor array. 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 extend Kalman filter, and the model selection by multiple model Rao-Blackwellized particle filter. The analytical relationship between target state and model is exploited to improve the efficiency and accuracy of the proposed algorithm. Finally, a nonlinear measurement model of multiple passive sensors is founded. The simulation results show that the proposed algorithm results in more accurate tracking than the IMM (Interacting Multiple Model) method.