Extensions of the SMC-PHD filters for jump Markov systems

• Published in: Signal processing 2012-06, Vol.92 (6), p.1422-1430
• Main Authors: Ouyang, Cheng, Ji, Hong-bing, Guo, Zhi-qiang
• Format: Article
• Language: English
• Subjects: Algorithms; Computer networks; Computer simulation; Density; Interacting multiple model; Java (programming language); Jump Markov system; Markov processes; Messages; Monte Carlo methods; Probability hypothesis density filter; Rao-Blackwellized particle filter
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2011.11.032

The probability hypothesis density (PHD) filter is a promising algorithm for multitarget tracking, which can be extended for jump Markov systems (JMS). Since the existing multiple model sequential Monte Carlo PHD (MM SMC-PHD) filter is not interacting, two extensions of the SMC-PHD filters are developed in this paper. The interacting multiple-model (IMM) SMC-PHD filter approximates the model conditional PHD of target states by particles, and performs the interaction by resampling without any a priori assumption of the noise. The IMM Rao-Blackwellized particle (RBP) PHD filter uses the idea of Rao-Blackwellized to further enhance the performance of target state estimation for JMS with mixed linear/nonlinear state space models. The simulation results show that the proposed algorithms have better performances than the existing MM SMC-PHD filter in terms of state filtering and target number estimation. ► The first study to extend the SMC-PHD filter to the interacting multiple model. ► The first study to embed the idea of Rao-Blackwellized to the SMC-PHD filter. ► The supplement to the BFG based GM-PHD filter for jump Markov systems.