Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking

Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking

In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptivel...

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Main Authors: Feng Yang, Yongqi Wang, Hao Chen, Pengyan Zhang, Yan Liang
Format: Article
Language:English
Published: MDPI AG 2016-10-01
Series:Sensors
Subjects:
multi-target tracking
multi-target state and track extraction
GMPHD filter
Online Access:http://www.mdpi.com/1424-8220/16/10/1666
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spelling doaj-f2172d68133c4d5e83cc873c3be63e9f2020-11-24T21:53:28ZengMDPI AGSensors1424-82202016-10-011610166610.3390/s16101666s16101666Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target TrackingFeng Yang0Yongqi Wang1Hao Chen2Pengyan Zhang3Yan Liang4School of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSouthwest China Research Institute of Electronic Equipment (SWIEE), Chengdu 610036, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaIn this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.http://www.mdpi.com/1424-8220/16/10/1666multi-target trackingmulti-target state and track extractionGMPHD filter
collection DOAJ
language English
format Article
sources DOAJ
author Feng Yang
Yongqi Wang
Hao Chen
Pengyan Zhang
Yan Liang
spellingShingle Feng Yang
Yongqi Wang
Hao Chen
Pengyan Zhang
Yan Liang
Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
Sensors
multi-target tracking
multi-target state and track extraction
GMPHD filter
author_facet Feng Yang
Yongqi Wang
Hao Chen
Pengyan Zhang
Yan Liang
author_sort Feng Yang
title Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
title_short Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
title_full Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
title_fullStr Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
title_full_unstemmed Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking
title_sort adaptive collaborative gaussian mixture probability hypothesis density filter for multi-target tracking
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2016-10-01
description In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.
topic multi-target tracking
multi-target state and track extraction
GMPHD filter
url http://www.mdpi.com/1424-8220/16/10/1666
work_keys_str_mv AT fengyang adaptivecollaborativegaussianmixtureprobabilityhypothesisdensityfilterformultitargettracking
AT yongqiwang adaptivecollaborativegaussianmixtureprobabilityhypothesisdensityfilterformultitargettracking
AT haochen adaptivecollaborativegaussianmixtureprobabilityhypothesisdensityfilterformultitargettracking
AT pengyanzhang adaptivecollaborativegaussianmixtureprobabilityhypothesisdensityfilterformultitargettracking
AT yanliang adaptivecollaborativegaussianmixtureprobabilityhypothesisdensityfilterformultitargettracking
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