Rough Sets Probabilistic Data Association Algorithm and its Application in Multi-target Tracking

A rough set probabilisfic data association （RS-PDA） algofitlml is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking appiication. In this new algorithm, the measurenlents lying in the intersection of t...

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Bibliographic Details
Published inDefence technology Vol. 9; no. 4; pp. 208 - 216
Main Authors Ni, Long-qiang, Gao, She-sheng, Feng, Peng-cheng, Zhao, Kai
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2013
Subjects
Data association
Data fusion
Rough set
Target tracking
仿真结果
多目标跟踪
应用
数据关联算法
时间消耗
概率
粗糙集理论
问题转化
Data fusion
Target tracking
Rough set
Data association
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Summary:A rough set probabilisfic data association （RS-PDA） algofitlml is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking appiication. In this new algorithm, the measurenlents lying in the intersection of two or more validation regions are allocated to the conesponding targets through rough set theory, and the multi-target tracking problem is transformed into a single target tracking after the classification of measurenaents lying in the intersection region. Severed typical multi-target tracking applications are given. The simulation results show that the algorithm can not ouly reduce the complexity and time consumption but also enhance the accuracy and stability of the tracking results.
Bibliography:10-1165/TJ
A rough set probabilisfic data association （RS-PDA） algofitlml is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking appiication. In this new algorithm, the measurenlents lying in the intersection of two or more validation regions are allocated to the conesponding targets through rough set theory, and the multi-target tracking problem is transformed into a single target tracking after the classification of measurenaents lying in the intersection region. Severed typical multi-target tracking applications are given. The simulation results show that the algorithm can not ouly reduce the complexity and time consumption but also enhance the accuracy and stability of the tracking results.
Rough set; Target tracking; Data association; Data fusion
ISSN:2214-9147
2214-9147
DOI:10.1016/j.dt.2013.11.004