A Gaussian Mixture PHD Filter for Jump Markov System Models

Syed Ahmed Pasha, Ba-Ngu Vo, Hoang Duong Tuan, Wing-Kin Ma
2009 IEEE Transactions on Aerospace and Electronic Systems  
The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to
more » ... multiple target case to accommodate births, deaths and switching dynamics. We then derive a closed form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.
doi:10.1109/taes.2009.5259174 fatcat:cvxyf4qdevff3a6mukewsiw5ke