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

作者：Fan Zhang, Zhenghuan Xia, Shichao Jin, Xin Liu, Zhilong Zhao, Chuang Zhang, Han Fu, Kang Xing, Zongqiang Liu, Changhu Xue, Tao Zhang, Zhiying Cui · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17233858 · 被引用次数：2 · 研究领域：Advanced SAR Imaging Techniques、Radar Systems and Signal Processing、Infrared Target Detection Methodologies

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 algorith...