Deep Learning-Assisted Tracking for Unresolvable UAV Swarm with Low Measurement Rates

As UAV technology has become more popular in various fields, tracking UAV swarms has gradually become a hot topic in recent years. Traditional tracking algorithms struggle to distinguish individual targets within the swarm under conditions of high clutter rates, low measurement rates and limited sensor resolution, resulting in limited available measurements. In this scenario, tracking each target within the swarm becomes impractical, necessitating a focus on the overall state of the UAV swarms rather than the individual states of its constituents. However, the existing methods have shown unsatisfactory tracking performance for UAV swarm in challenging scenarios with low measurement rates. To enhance the tracking performance in such challenging scenarios, in this paper, we propose a UAV swarm tracking method, called ST-UST, which combines a deep learning network named swin transformer with the Kalman filter. The core element of the proposed ST-UST method lies in the utilization of swin transformer to process images derived from noisy point clouds. In this method, swin transformer can achieve the inference of swarm shape parameters, and the Kalman filter is utilized to estimate swarm kinematic parameters. Experimental results show that, in comparison with the existing methods, the proposed ST-UST method has significant competitiveness in challenging scenarios with low measurement rates, and the maximum tracking accuracy can be improved by nearly 20.7%.