Feature-Weighted Track-to-Track Association Based on Adaptive Fuzzy C-Shell Cluster

被引：0
|
作者：
Zhang, Zhemin [1 ]
Chen, Chen [1 ]
机构：
[1] Beijing Inst Technol, Sch Automat, Beijing, Peoples R China
来源：
2015 SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP) | 2015年
关键词：
data fusion; track-to-track association; feature selection; fuzzy clustering; CORRELATION ALGORITHM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The traditional track-to-track association (track fusion) algorithm mostly focuses on single and straight tracks, while the tracks generated by maneuvering flight, like curves, have not been researched deeply. This paper reviews current techniques of track-to-track association and improves a method, based on Adaptive Fuzzy C-Shell cluster (AFCS), which can be used among those situations where target leaves curve-like tracks. This method collects data from distributed multi-sensors network to generate track features, then uses feature-weighted AFCS algorithm to achieve track fusion. The experiment shows the proposed approach performed well under certain circumstances.
引用
收藏
页码：161 / 165
页数：5
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