Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar

QIAN Yuning; CHEN Yawei; LI Gui

doi:10.11999/JEIT220895

Volume 45 Issue 8

Aug. 2023

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QIAN Yuning, CHEN Yawei, LI Gui. Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2991-3001. doi: 10.11999/JEIT220895

Citation:

QIAN Yuning, CHEN Yawei, LI Gui. Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2991-3001. doi: 10.11999/JEIT220895

Citation:

QIAN Yuning, CHEN Yawei, LI Gui. Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2991-3001. doi: 10.11999/JEIT220895

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Target Association and Tracking Approach Based on Historical Kinematic Characteristics and SVM Spectrum Classification for Passive Sonar

doi: 10.11999/JEIT220895

Nanjing Research Institute of Electronics Technology, Nanjing 210039, China

Received Date: 2022-07-04
Accepted Date: 2022-12-20
Rev Recd Date: 2022-12-02

Available Online: 2022-12-23

Publish Date: 2023-08-21

Abstract

Abstract

In order to solve the crossing target tracking problem for passive sonar, a target association and tracking approach based on Historical kinematic characteristics and SVM (His-SVM) spectrum classification is presented, which combines the improved kinematic feature association method with the revised signal feature association method. The historical bearing changing rate is firstly extracted from historical track points to be used as a main feature for the overlapping target association and tracking. Furthermore, the SVM model, which is trained by the spectrum of track points, is utilized to classify the close trace points and each trace points can be assigned to different targets according to the classification results. Finally, the framework of the crossing target tracking algorithm is constructed by integrating historical kinematic characteristics with the SVM spectrum classification. The results of simulation studies verify the effectiveness of the proposed approach for close target association and crossing target tracking, and indicate that the tracking performance of the proposed approach is better than the traditional kinematic feature association method.
- Passive sonar,
- Target association and tracking,
- Historical kinematic characteristics,
- SVM spectrum classification

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References(18)

References

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