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A Gaussian Mixture PHD Filter for Jump Markov System Models

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Open access
Authors
Pasha, S.
Vo, Ba-Ngu
Tuan, H.
Ma, W.
Date
2009
Type
Journal Article

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Pasha, S. and Vo, B. and Tuan, H. and Ma, W. 2009. A Gaussian Mixture PHD Filter for Jump Markov System Models. IEEE Transactions on Aerospace and Electronic Systems. 45 (3): pp. 919-936.
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IEEE Transactions on Aerospace and Electronic Systems
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Department of Electrical and Computer Engineering
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Copyright © 2009. IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Abstract

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.