A Multi-Feature Adaptive Association Method for High-Frequency Radar Target Tracking

High-frequency surface wave radar (HFSWR) with a small aperture suffers from limited azimuth resolution, which often leads to association errors and trajectory fragmentation in complex scenarios involving sea clutter and intersecting target tracks. To address this issue, we propose a multi-feature adaptive association method that integrates the target direction cosine features and motion parameters to construct an improved association gate suitable for targets in uniform linear motion. For multiple plots within the association gate, the method evaluates their similarity to the trajectory by combining multiple feature parameters such as great-circle distance and Mahalanobis distance. An adaptive weighting strategy is employed according to the trajectory state to select the most similar plot for association. For trajectories without associated plots, the method maintains them based on a motion model and Kalman predictor. Experimental results demonstrate that the trajectories generated by this method last longer than those produced by traditional association methods, confirming that the proposed approach effectively suppresses trajectory fragmentation and false tracking, thereby enhancing the continuity and reliability of HFSWR target tracking.