An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System
The problem of duplicate track determination called “Track-to-Track association” occurs when a target is reported by different sensors, and it is regarded as one of the most important challenges in distributed multi-sensor tracking systems. The present study aimed to propose a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8834774/ |
| _version_ | 1818949166062108672 |
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| author | Mousa Nazari Saeid Pashazadeh Leyli Mohammad-Khanli |
| author_facet | Mousa Nazari Saeid Pashazadeh Leyli Mohammad-Khanli |
| author_sort | Mousa Nazari |
| collection | DOAJ |
| description | The problem of duplicate track determination called “Track-to-Track association” occurs when a target is reported by different sensors, and it is regarded as one of the most important challenges in distributed multi-sensor tracking systems. The present study aimed to propose a density-based fuzzy clustering method for solving the track-to-track association problem in distributed multi-sensor tracking systems. Unlike the previously published solutions, the proposed method does not need any information about the number of targets, due to the use of the density-based clustering approach. Proposed method has low computational overhead and can be used in real-time tracking systems. In addition, the proposed method uses the maximum entropy approach to determine the membership degree of single target related tracks and combines them. This paper presents three scenarios including sensors with complete and incomplete overlapping by considering the bias and a different number of sensors and targets for evaluating the proposed method based on the Monte Carlo simulation. The results indicate the improvement of the efficiency in comparison with the FTF approach. The efficiency of proposed method's results is close to the results of Bayesian minimum mean square error criterion that gives best possible results. |
| first_indexed | 2024-12-20T08:58:22Z |
| format | Article |
| id | doaj.art-a156bda6afa74cd3b356600e31eac01b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T08:58:22Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a156bda6afa74cd3b356600e31eac01b2022-12-21T19:45:56ZengIEEEIEEE Access2169-35362019-01-01713597213598110.1109/ACCESS.2019.29411848834774An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking SystemMousa Nazari0https://orcid.org/0000-0001-6856-7888Saeid Pashazadeh1https://orcid.org/0000-0002-8949-9180Leyli Mohammad-Khanli2https://orcid.org/0000-0001-7394-5054Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranThe problem of duplicate track determination called “Track-to-Track association” occurs when a target is reported by different sensors, and it is regarded as one of the most important challenges in distributed multi-sensor tracking systems. The present study aimed to propose a density-based fuzzy clustering method for solving the track-to-track association problem in distributed multi-sensor tracking systems. Unlike the previously published solutions, the proposed method does not need any information about the number of targets, due to the use of the density-based clustering approach. Proposed method has low computational overhead and can be used in real-time tracking systems. In addition, the proposed method uses the maximum entropy approach to determine the membership degree of single target related tracks and combines them. This paper presents three scenarios including sensors with complete and incomplete overlapping by considering the bias and a different number of sensors and targets for evaluating the proposed method based on the Monte Carlo simulation. The results indicate the improvement of the efficiency in comparison with the FTF approach. The efficiency of proposed method's results is close to the results of Bayesian minimum mean square error criterion that gives best possible results.https://ieeexplore.ieee.org/document/8834774/Density clusteringdistributed target trackingmulti sensor fusiontrack association |
| spellingShingle | Mousa Nazari Saeid Pashazadeh Leyli Mohammad-Khanli An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System IEEE Access Density clustering distributed target tracking multi sensor fusion track association |
| title | An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System |
| title_full | An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System |
| title_fullStr | An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System |
| title_full_unstemmed | An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System |
| title_short | An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System |
| title_sort | adaptive density based fuzzy clustering track association for distributed tracking system |
| topic | Density clustering distributed target tracking multi sensor fusion track association |
| url | https://ieeexplore.ieee.org/document/8834774/ |
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