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Remote Sensing
Volume 17
Issue 23
10.3390/rs17233858
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Ship Target Feature Detection of Airborne Scanning Radar Based on Trajectory Prediction Integration

by
Fan Zhang
1,2,
Zhenghuan Xia
1,2,*,
Shichao Jin
1,2,
Xin Liu
1,2,
Zhilong Zhao
1,2,
Chuang Zhang
1,2,
Han Fu
1,2,
Kang Xing
1,2,
Zongqiang Liu
1,2,
Changhu Xue
1,2,
Tao Zhang
1,2 and
Zhiying Cui
1,2
1
State Key Laboratory of Space Information System and Integrated Application, Beijing, 100095, China
2
Beijing Institute of Satellite Information Engineering, Beijing 100095, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(23), 3858; https://doi.org/10.3390/rs17233858 (registering DOI)
Submission received: 27 September 2025 / Revised: 22 November 2025 / Accepted: 26 November 2025 / Published: 28 November 2025
Versions Notes

Abstract

In order to address the challenges faced by airborne scanning radars in detecting maritime ship targets, such as low signal-to-clutter ratios and the strong spatio-temporal non-stationarity of sea clutter, this paper proposes a multi-feature detection method based on trajectory prediction integration. First, the Margenau–Hill Spectrogram (MHS) is employed for time–frequency analysis and uniformization processing. The extraction of features is conducted across three dimensions: energy intensity, spatial clustering, and distributional disorder. The metrics employed in this study include ridge integral (RI), maximum size of connected regions (MS), and scanning slice time–frequency entropy (SSTFE). Feature normalization is achieved via reference units to eliminate dynamic range variations. Secondly, a trajectory prediction matrix is constructed to correlate target cross-scan distance variations. When combined with a scan weight matrix that dynamically adjusts multi-frame contributions, this approach enables effective accumulation of target features across multiple scans. Finally, the greedy convex hull algorithm is used to complete target detection with a controllable false alarm rate. The validation process employs real-world data from a C-band dual-polarization airborne scanning radar. The findings indicate a 36.11% enhancement in the number of successful detections in comparison to the conventional single-frame three-feature detection method. Among the extant scanning algorithms, this approach evinces optimal feature space separability and detection performance, thus offering a novel pathway for maritime target detection using airborne scanning radars.
Keywords: target detection; airborne scanning radar; feature-based detection; multi-frame processing target detection; airborne scanning radar; feature-based detection; multi-frame processing

Share and Cite

MDPI and ACS Style

Zhang, F.; Xia, Z.; Jin, S.; Liu, X.; Zhao, Z.; Zhang, C.; Fu, H.; Xing, K.; Liu, Z.; Xue, C.; et al. Ship Target Feature Detection of Airborne Scanning Radar Based on Trajectory Prediction Integration. Remote Sens. 2025, 17, 3858. https://doi.org/10.3390/rs17233858

AMA Style

Zhang F, Xia Z, Jin S, Liu X, Zhao Z, Zhang C, Fu H, Xing K, Liu Z, Xue C, et al. Ship Target Feature Detection of Airborne Scanning Radar Based on Trajectory Prediction Integration. Remote Sensing. 2025; 17(23):3858. https://doi.org/10.3390/rs17233858

Chicago/Turabian Style

Zhang, Fan, Zhenghuan Xia, Shichao Jin, Xin Liu, Zhilong Zhao, Chuang Zhang, Han Fu, Kang Xing, Zongqiang Liu, Changhu Xue, and et al. 2025. "Ship Target Feature Detection of Airborne Scanning Radar Based on Trajectory Prediction Integration" Remote Sensing 17, no. 23: 3858. https://doi.org/10.3390/rs17233858

APA Style

Zhang, F., Xia, Z., Jin, S., Liu, X., Zhao, Z., Zhang, C., Fu, H., Xing, K., Liu, Z., Xue, C., Zhang, T., & Cui, Z. (2025). Ship Target Feature Detection of Airborne Scanning Radar Based on Trajectory Prediction Integration. Remote Sensing, 17(23), 3858. https://doi.org/10.3390/rs17233858

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