GM-PHD filter combined with track-estimate association and numerical interpolation
type: Item
instance of: scholarly article
title: GM-PHD filter combined with track-estimate association and numerical interpolation
zbMATH Open document ID: 1395.62289
DOI: 10.1155/2015/629023
author: Bugao Xu
author: Lili Ma
author: Rui Sun
author: Jin-Guang Chen
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: For the standard Gaussian mixture probability hypothesis density (GM-PHD) filter, the number of targets can be overestimated if the clutter rate is too high or underestimated if the detection rate is too low. These problems seriously affect the accuracy of multitarget tracking for the number and the value of measurements and clutters cannot be distinguished and recognized. Therefore, we proposed an improved GM-PHD filter to tackle these problems. Firstly, a track-estimate association was implemented in the filtering process to detect and remove false-alarm targets. Secondly, a numerical interpolation technique was used to compensate the missing targets caused by low detection rate. At the end of this paper, simulation results were presented to demonstrate the proposed GM-PHD algorithm is more effective in estimating the number and state of targets than the previous ones.
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 6926848
Wikidata QID: Q59119172
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/629023
OpenAlex ID: W1607458892
cites work: Mathematics of data fusion
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description: scientific article; zbMATH DE number 6926848