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 the 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.
A Gaussian Mixture PHD Filter for Jump Markov System Models
IEEE Transactions on Aerospace and Electronic Systems ; 45 , 3 ; 919-936
2009-07-01
2487717 byte
Article (Journal)
Electronic Resource
English
A Gaussian Mixture PHD Filter for Jump Markov System Models
Online Contents | 2009
|Gaussian Mixture Approximation by Another Gaussian Mixture for `Blob' Filter Re-Sampling
British Library Conference Proceedings | 2010
|