Ship Target Feature Detection of Airborne Scanning Radar Based on Trajectory Prediction Integration
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| Publicado en: | Remote Sensing vol. 17, no. 23 (2025), p. 3858-3883 |
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| Autor principal: | |
| Otros Autores: | , , , , , , , , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resumen: | <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>A multi-feature detection method based on trajectory prediction integration is proposed for airborne elevation-scanning radar ship target detection to realize multi-scan feature accumulation. <list-item> Validated with C-band dual-polarization airborne elevation-scanning radar real data, the method outperforms conventional single-frame three-feature detection and other existing scanning algorithms. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>The method addresses the low signal-to-clutter ratio and strong spatio-temporal non-stationarity of sea clutter that plague airborne elevation-scanning radar detection, making up for the defects of existing scanning algorithms. <list-item> Measured data show that VH polarization outperforms VV polarization in detection, beam position affects performance, and refining beam position segmentation of continuous-scan radar can further improve detection, guiding radar parameter configuration. </list-item> 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. |
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| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs17233858 |
| Fuente: | Advanced Technologies & Aerospace Database |