Radar target tracking based on motion characteristic and distribution pattern matching

The mitigation of false alarm rates under real-world radar operating conditions represents a critical challenge in advancing radar target detection algorithms. This study proposes that utilizing multi-frame correlation information through radar target tracking constitutes an effective solution for suppressing false alarms. We present a radar target tracking methodology that integrates motion characteristic and distribution pattern matching, effectively leveraging multi-frame radar measurements and echo amplitude information. This approach enables false alarm reduction through trajectory consistency validation. Specifically, the method operates in two stages: First, state filtering based on motion characteristics is applied to predict potential candidate regions for previous detections within the current frame. Subsequently, within these candidate regions, a deep learning-based similarity evaluation framework employing a self-supervised Siamese network performs distribution pattern matching to establish optimal data associations. Experimental validation demonstrates that the proposed method achieves a 5.82% improvement in F1-score over benchmark algorithms, confirming its enhanced detection reliability and operational effectiveness.