An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm
In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied...
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MDPI AG
2019-01-01
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Online Access: | http://www.mdpi.com/1424-8220/19/2/366 |
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author | Han Shen-Tu Hanming Qian Dongliang Peng Yunfei Guo Ji-An Luo |
author_facet | Han Shen-Tu Hanming Qian Dongliang Peng Yunfei Guo Ji-An Luo |
author_sort | Han Shen-Tu |
collection | DOAJ |
description | In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency. |
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institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-04-12T19:37:50Z |
publishDate | 2019-01-01 |
publisher | MDPI AG |
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spelling | doaj.art-7b9359554678452a84edccfc752f7e7e2022-12-22T03:19:10ZengMDPI AGSensors1424-82202019-01-0119236610.3390/s19020366s19020366An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD AlgorithmHan Shen-Tu0Hanming Qian1Dongliang Peng2Yunfei Guo3Ji-An Luo4Institution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, ChinaScience and Technology on Near-surface Detection Laboratory, Wuxi 214035, ChinaInstitution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, ChinaInstitution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, ChinaInstitution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, ChinaIn this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency.http://www.mdpi.com/1424-8220/19/2/366random finite setsmulti-sensor multi-target trackingmulti-sensor data fusingGM-PHD |
spellingShingle | Han Shen-Tu Hanming Qian Dongliang Peng Yunfei Guo Ji-An Luo An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm Sensors random finite sets multi-sensor multi-target tracking multi-sensor data fusing GM-PHD |
title | An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm |
title_full | An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm |
title_fullStr | An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm |
title_full_unstemmed | An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm |
title_short | An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm |
title_sort | unbalanced weighted sequential fusing multi sensor gm phd algorithm |
topic | random finite sets multi-sensor multi-target tracking multi-sensor data fusing GM-PHD |
url | http://www.mdpi.com/1424-8220/19/2/366 |
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