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A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets †
In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectivel...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201257/ https://www.ncbi.nlm.nih.gov/pubmed/34200379 http://dx.doi.org/10.3390/s21113932 |
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author | Gao, Yiyue Jiang, Defu Zhang, Chao Guo, Su |
author_facet | Gao, Yiyue Jiang, Defu Zhang, Chao Guo, Su |
author_sort | Gao, Yiyue |
collection | PubMed |
description | In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity. |
format | Online Article Text |
id | pubmed-8201257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82012572021-06-15 A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets † Gao, Yiyue Jiang, Defu Zhang, Chao Guo, Su Sensors (Basel) Article In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity. MDPI 2021-06-07 /pmc/articles/PMC8201257/ /pubmed/34200379 http://dx.doi.org/10.3390/s21113932 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Yiyue Jiang, Defu Zhang, Chao Guo, Su A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets † |
title | A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets † |
title_full | A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets † |
title_fullStr | A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets † |
title_full_unstemmed | A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets † |
title_short | A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets † |
title_sort | labeled gm-phd filter for explicitly tracking multiple targets † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201257/ https://www.ncbi.nlm.nih.gov/pubmed/34200379 http://dx.doi.org/10.3390/s21113932 |
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