Combing IMM Filtering and MHT Data Association for Multitarget Tracking
SSST '97 Proceedings of the 29th Southeastern Symposium on System Theory (SSST '97)
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
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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.