Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks
The uncertainty problem in sensor track to local track association is a difficult problem in distributed sensor networks, particularly when there is a big difference of sensors’ tracking performance. To solve this problem, a weighted fuzzy track association (FTA) method based on Dempster–Shafer theo...
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Series: | International Journal of Distributed Sensor Networks |
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doaj-98d4b697e65d4a339bf3feca693509f42020-11-25T02:55:15ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772016-07-011210.1177/1550147716658599Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networksLixin Fan0En Fan1Changhong Yuan2Keli Hu3Department of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaATR Key Laboratory, Shenzhen University, Shenzhen, ChinaAir Defense Forces Academy, Zhengzhou, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaThe uncertainty problem in sensor track to local track association is a difficult problem in distributed sensor networks, particularly when there is a big difference of sensors’ tracking performance. To solve this problem, a weighted fuzzy track association (FTA) method based on Dempster–Shafer theory is proposed. In the proposed method, five characteristics of sensor tracks from different sensors are established, and meanwhile their belief functions are defined to determine the corresponding beliefs. Considering the different effects of sensor tracks on track association, the reliabilities of sensor tracks are further presented and their magnitudes can be calculated by the combination belief function defined. Then, these reliabilities are used to reconstruct the fuzzy association degrees by the FTA method. The proposed method has an advantage that it can dynamically allocate the weight of each sensor track in association decision according to its characteristics. The performance of the proposed method is evaluated by using two experiments with simulation data in manoeuvring and uniform situations. It is found to be better than those of other two track association methods in tracking accuracy.https://doi.org/10.1177/1550147716658599 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lixin Fan En Fan Changhong Yuan Keli Hu |
spellingShingle |
Lixin Fan En Fan Changhong Yuan Keli Hu Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks International Journal of Distributed Sensor Networks |
author_facet |
Lixin Fan En Fan Changhong Yuan Keli Hu |
author_sort |
Lixin Fan |
title |
Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks |
title_short |
Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks |
title_full |
Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks |
title_fullStr |
Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks |
title_full_unstemmed |
Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks |
title_sort |
weighted fuzzy track association method based on dempster–shafer theory in distributed sensor networks |
publisher |
SAGE Publishing |
series |
International Journal of Distributed Sensor Networks |
issn |
1550-1477 |
publishDate |
2016-07-01 |
description |
The uncertainty problem in sensor track to local track association is a difficult problem in distributed sensor networks, particularly when there is a big difference of sensors’ tracking performance. To solve this problem, a weighted fuzzy track association (FTA) method based on Dempster–Shafer theory is proposed. In the proposed method, five characteristics of sensor tracks from different sensors are established, and meanwhile their belief functions are defined to determine the corresponding beliefs. Considering the different effects of sensor tracks on track association, the reliabilities of sensor tracks are further presented and their magnitudes can be calculated by the combination belief function defined. Then, these reliabilities are used to reconstruct the fuzzy association degrees by the FTA method. The proposed method has an advantage that it can dynamically allocate the weight of each sensor track in association decision according to its characteristics. The performance of the proposed method is evaluated by using two experiments with simulation data in manoeuvring and uniform situations. It is found to be better than those of other two track association methods in tracking accuracy. |
url |
https://doi.org/10.1177/1550147716658599 |
work_keys_str_mv |
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