A Target Sensing and Visual Tracking Method for Countering Unmanned Aerial Vehicle Swarm

Anti unmanned aerial vehicle (UAV) swarm system includes target detection and strike, where detection involves sensing and tracking the target. Aiming at the problems of low detection performance and unstable tracking in traditional methods, a multiobject visual tracking method for low altitude UAV swarm is proposed. First, an efficient channel attention (ECA) module is introduced into the backbone to enhance the ability of feature extraction. Meanwhile, Swin Transformer block is applied in the neck to enable the model to better capture different local information. Furthermore, alpha intersection over union ( $\alpha $ -IoU) is used to optimize loss function to facilitate network convergence and improve positioning accuracy. Finally, BYTE strategy is used for data association to increase the integrity of trajectories, thereby achieving accurate and robust tracking of UAV swarm targets. The experiments show that the tracking accuracy on the UAV swarm test dataset reaches 78.2%, which is about 3% higher than the current advanced model. And it is 12.3%, 6.8%, 10%, and 6% higher than the FairMOT, SORT, OC-SORT, and DeepSORT, proving the effectiveness of the multi-UAV visual tracking method.