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Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

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Open access
Authors
Panta, K.
Clark, D.
Vo, Ba-Ngu
Date
2009
Type
Journal Article

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Panta, K. and Clark, D. and Vo, B. 2009. Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter. IEEE Transactions on Aerospace and Electronic Systems. 45 (3): pp. 1003-1016.
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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 Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.