An Efficient Track Management Scheme for the Gaussian-Mixture Probability Hypothesis Density Tracker

Abstract

The Gaussian mixture probability hypothesis density (GM-PHD) filter is a closed-form solution for the probability hypothesis density (PHD) filter, 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-detections… (More)

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Cite this paper

@article{Panta2006AnET, title={An Efficient Track Management Scheme for the Gaussian-Mixture Probability Hypothesis Density Tracker}, author={Kusha Panta and Ba-Ngu-Vo and D. Clark}, journal={2006 Fourth International Conference on Intelligent Sensing and Information Processing}, year={2006}, pages={230-235} }