Clustering Based Multiple Hypothesis Multi-target Localization

被引:1
作者
Wang, Huan [1 ]
Liu, Guanghong [1 ]
Zhang, De [1 ]
Han, Kuoye [1 ]
Jiang, Mian [1 ]
机构
[1] Informat Sci Acad China Elect Technol Grp Corp, Inst Informat Syst Technol, Beijing, Peoples R China
来源
2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE) | 2017年
关键词
cooperative localization; signal-level; multiple hypothesis; multi-target localization;
D O I
10.1109/ICISCE.2017.190
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cooperative detection system, which combines self organizing network and single radar system, improves the ability of cooperative localization by distributed multi-node using multi-direction scattering power of target. Multiple hypothesis (MH)-Based Algorithm for Target Localization finds all possible targets using multi-path echo information with unknown number of targets, and realizes multi-target localization by generating global hypothesis. However, a large number of targets will cause the large computational complexity of MH-Based Algorithm for Target Localization. For this problem, this paper proposes a new method of Clustering Based Multiple Hypothesis Multi-target Localization, which decomposes a large number of hypotheses into several independent clusters, and hypothesis updating, hypothesis pruning and global hypothesis generating will be implemented independently in each cluster. Compared with MH-Based Algorithm for Target Localization, the proposed method increases the computation efficiency.
引用
收藏
页码:898 / 901
页数:4
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