Clustering Based Multiple Hypothesis Multi-Target Localization
2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE)(2017)
Abstract
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.
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Key words
cooperative localization, signal-level, multiple hypothesis, multi-target localization
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