An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking
In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6842 |
| _version_ | 1827701094901350400 |
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| author | Junhai Luo Zhiyan Wang Yanping Chen Man Wu Yang Yang |
| author_facet | Junhai Luo Zhiyan Wang Yanping Chen Man Wu Yang Yang |
| author_sort | Junhai Luo |
| collection | DOAJ |
| description | In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy. |
| first_indexed | 2024-03-10T14:25:57Z |
| format | Article |
| id | doaj.art-22dd191e865c4145a838d0bfe3451fd9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:25:57Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-22dd191e865c4145a838d0bfe3451fd92023-11-20T22:56:38ZengMDPI AGSensors1424-82202020-11-012023684210.3390/s20236842An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target TrackingJunhai Luo0Zhiyan Wang1Yanping Chen2Man Wu3Yang Yang4School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaIn this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.https://www.mdpi.com/1424-8220/20/23/6842multi-sensor fusiontarget trackingimproved unscented particle filterdata fusion |
| spellingShingle | Junhai Luo Zhiyan Wang Yanping Chen Man Wu Yang Yang An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking Sensors multi-sensor fusion target tracking improved unscented particle filter data fusion |
| title | An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking |
| title_full | An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking |
| title_fullStr | An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking |
| title_full_unstemmed | An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking |
| title_short | An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking |
| title_sort | improved unscented particle filter approach for multi sensor fusion target tracking |
| topic | multi-sensor fusion target tracking improved unscented particle filter data fusion |
| url | https://www.mdpi.com/1424-8220/20/23/6842 |
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