A NOVEL DATA ASSOCIATION ALGORITHM BASED ON FCM-DFA

Zhenkai Zhang, Jialin Cheng, Jie Wu
<span title="">2017</span> <i title="ICIC International"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wdc7itdnm5ekhoql3paw7mmr4q" style="color: black;">ICIC Express Letters</a> </i> &nbsp;
In order to obtain higher correct correlation rate under the condition of high track density in multiple target tracking system, it is very necessary to improve association accuracy. In this paper, a data association algorithm based on fuzzy c-means clustering (FCM) and discriminative feature algorithm (DFA) is presented. Every time the membership degree matrix between measurements and clustering center will be computed. Membership degree matrix is designed to represent the association degree
more &raquo; ... tween the measurements and tracks. According to the maximal element of each sensor's membership degree matrix, if the measurement is not associated with any track, the discriminative feature will be utilized to determine the measurement ownership. The proposed algorithm is realized in the simulation experiment, which compares FCM-DFA with the nearest neighbors data association algorithm (NNDA), and the simulation result indicates that the proposed algorithm has better correlation rate when the tracks are crossed and the tracking accuracy is better.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24507/icicel.11.10.1489">doi:10.24507/icicel.11.10.1489</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5x2jsncabrg43nwjudsaqs3cay">fatcat:5x2jsncabrg43nwjudsaqs3cay</a> </span>
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