Extensions of the SMC-PHD filters for jump Markov systems |
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Authors: | Cheng OuyangHong-bing Ji Zhi-qiang Guo |
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Affiliation: | School of Electronic Engineering, Xidian University, Xi'an 710071, China |
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Abstract: | 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. |
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Keywords: | Probability hypothesis density filter Interacting multiple model Jump Markov system Rao-Blackwellized particle filter |
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