文献类型：期刊文献

英文题名：Weighted fuzzy track association method based on Dempster-Shafer theory in distributed sensor networks

作者：Fan, Lixin[1];Fan, En[1,2];Yuan, Changhong[3];Hu, Keli[1]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]Shenzhen Univ, ATR Key Lab, Shenzhen, Peoples R China;[3]Air Def Forces Acad, Zhengzhou, Peoples R China

年份：2016

卷号：12

期号：7

起止页码：1

外文期刊名：INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS

收录：SCI-EXPANDED(收录号：WOS:000383389000031)、、EI(收录号：20163202696232)、Scopus(收录号：2-s2.0-84982696406)、WOS

基金：The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the National Natural Science Foundation of China (no. 61272034), the Startup Project of Doctor Science Research of Shaoxing University (no. 20145021 and no. 20155015), the Science Project of Shaoxing University (no. 2015LG1006) and the Public Welfare Technology Application Research-Industrial Project of Zhejiang Province (no. 2016C31082).

语种：英文

外文关键词：Information fusion; distributed sensor network; multiple target tracking; track association; Dempster-Shafer theory

外文摘要：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.