Modified Bayesian Group Target Track Initiation Algorithm Based on Algebraic Graph Theory

Qi JIANG; Rui WANG; Chao ZHOU; Tianran ZHANG; Cheng HU

doi:10.11999/JEIT200449

Volume 43 Issue 3

Mar. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(3): 531-538

Qi JIANG, Rui WANG, Chao ZHOU, Tianran ZHANG, Cheng HU. Modified Bayesian Group Target Track Initiation Algorithm Based on Algebraic Graph Theory[J]. Journal of Electronics & Information Technology, 2021, 43(3): 531-538. doi: 10.11999/JEIT200449

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Qi JIANG, Rui WANG, Chao ZHOU, Tianran ZHANG, Cheng HU. Modified Bayesian Group Target Track Initiation Algorithm Based on Algebraic Graph Theory[J]. Journal of Electronics & Information Technology, 2021, 43(3): 531-538. doi: 10.11999/JEIT200449

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Modified Bayesian Group Target Track Initiation Algorithm Based on Algebraic Graph Theory

doi: 10.11999/JEIT200449

1.
Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2.
Key Laboratory of Electronic and Information Technology in Satellite Navigation(Beijing Institute of Technology), Ministry of Education, Beijing 100081, China

Funds: The National Natural Science Foundation of China (31727901)

Received Date: 2020-06-04
Rev Recd Date: 2020-12-08

Available Online: 2020-12-18

Publish Date: 2021-03-22

Abstract

Abstract

Group target tracking is an efficient method to measure the states of airborne flocks. The first step of group target tracking is track initiation, including target clustering and track promotion. The state-of-the-art algorithms require mutual similarity between targets for clustering procedure, and track may be wrongly rejected due to the large residual of equivalent measurement. A modified Bayesian group track initiation algorithm based on algebraic graph theory is proposed. The clustering of measurement sets in surveillance volume is achieved by introducing the algebraic graph theory. The rejection of true track is avoided by modify the definition of classical Bayesian likelihood ratio. Results from actual field tests demonstrate the capability of clustering group targets precisely and promoting group tracks effectively.
- Group target tracking,
- Track initiation,
- Target clustering,
- Track promotion

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References(24)

References

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