کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
562789 | 875439 | 2012 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Extensions of the SMC-PHD filters for jump Markov systems Extensions of the SMC-PHD filters for jump Markov systems](/preview/png/562789.png)
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.
Journal: Signal Processing - Volume 92, Issue 6, June 2012, Pages 1422–1430