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A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers
In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter m...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948621/ https://www.ncbi.nlm.nih.gov/pubmed/29617348 http://dx.doi.org/10.3390/s18041095 |
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author | Liu, Zhuowei Chen, Shuxin Wu, Hao He, Renke Hao, Lin |
author_facet | Liu, Zhuowei Chen, Shuxin Wu, Hao He, Renke Hao, Lin |
author_sort | Liu, Zhuowei |
collection | PubMed |
description | In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers. |
format | Online Article Text |
id | pubmed-5948621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59486212018-05-17 A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers Liu, Zhuowei Chen, Shuxin Wu, Hao He, Renke Hao, Lin Sensors (Basel) Article In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers. MDPI 2018-04-04 /pmc/articles/PMC5948621/ /pubmed/29617348 http://dx.doi.org/10.3390/s18041095 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Zhuowei Chen, Shuxin Wu, Hao He, Renke Hao, Lin A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers |
title | A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers |
title_full | A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers |
title_fullStr | A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers |
title_full_unstemmed | A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers |
title_short | A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers |
title_sort | student’s t mixture probability hypothesis density filter for multi-target tracking with outliers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948621/ https://www.ncbi.nlm.nih.gov/pubmed/29617348 http://dx.doi.org/10.3390/s18041095 |
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