An Adaptive Weak Target Detection Method Using Joint Detection and Tracking for Compact High Frequency Surface Ware Radar

SUN Weifeng; LI Xiaotong; JI Yonggang; DAI Yongshou

doi:10.11999/JEIT220811

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(8): 2955-2964

SUN Weifeng, LI Xiaotong, JI Yonggang, DAI Yongshou. An Adaptive Weak Target Detection Method Using Joint Detection and Tracking for Compact High Frequency Surface Ware Radar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2955-2964. doi: 10.11999/JEIT220811

Citation:

SUN Weifeng, LI Xiaotong, JI Yonggang, DAI Yongshou. An Adaptive Weak Target Detection Method Using Joint Detection and Tracking for Compact High Frequency Surface Ware Radar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2955-2964. doi: 10.11999/JEIT220811

Citation:

SUN Weifeng, LI Xiaotong, JI Yonggang, DAI Yongshou. An Adaptive Weak Target Detection Method Using Joint Detection and Tracking for Compact High Frequency Surface Ware Radar[J]. Journal of Electronics & Information Technology, 2023, 45(8): 2955-2964. doi: 10.11999/JEIT220811

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An Adaptive Weak Target Detection Method Using Joint Detection and Tracking for Compact High Frequency Surface Ware Radar

doi: 10.11999/JEIT220811

College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

Funds: The National Natural Science Foundation of China (62071493, 61831010)

Received Date: 2022-06-20
Rev Recd Date: 2023-02-06

Available Online: 2023-02-08

Publish Date: 2023-08-21

Abstract

Abstract

The low transmission power and low signal-to-noise ratio increase the challenges of target detection for compact High-Frequency Surface Wave Radar (HFSWR). Track fragmentation often occurs due to missed detections during the tracking procedure. An adaptive weak target detection method using joint detection and tracking is suggested to enhance its detection performance. The tracker will communicate back the current target prediction state to the detector when it discovers that a target track can not be connected to any new plot. The detector establishes a local detection gate on the Range-Doppler (R-D) spectrum, and the detection background is perceived using the binary hypothesis test. According to the detected background, an appropriate detection threshold adjustment method is used to lower the Constant False Alarm Rate (CFAR) detection threshold and determines whether a weak target can be detected. The newly generated plot is obtained after the azimuth estimate and transmitted to the tracker for additional processing if a target is detected. The experimental results with field data reveal that the track length obtained by the proposed method is 29.76% longer than that of the detection before tracking methods, and the tracking time increases by 19.25 minutes on average.
- Compact HFSWR,
- Weak target detection,
- Joint detection and tracking,
- Detection background perception,
- Multi-strategy detection

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References(20)

References

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