Radar-based Interactive Multi-Model Multi-Target Tracking Algorithm for UAV Swarms

With the gradual opening of low-altitude airspace and the rapid development of UAV technology, large-scale UAV swarms are increasingly used in logistics, inspection, and security scenarios. To achieve accurate and robust tracking of highly dynamic, high-density, and strongly interactive UAV swarms, this study proposes a radar-based interactive multi-model multi-target tracking algorithm. Point cloud quality is improved by integrating velocity vector density clustering with adaptive constant false alarm rate detection. An interactive multi-model framework incorporating uniform velocity, uniform acceleration, and coordinated turning modes is established, together with a group potential field–driven state transition mechanism. Adaptive thresholding and dynamic track splitting and merging strategies are further introduced to enhance tracking stability. Experimental results show that the proposed method achieves an average distance RMSE of 1.47 m and a speed RMSE of 1.18 m/s, representing reductions of 49.8% and 42.3% compared with the traditional joint probability data association algorithm. The average tracking accuracy reaches 88.19% and remains 84.31% under a clutter density of 80 points/scan, while the average computation time is 36.92 ms, satisfying real-time requirements. The results demonstrate improved accuracy and stability for low-altitude UAV surveillance in high-density urban scenarios.