A MUSIC-based Method for Detection and Localization of UAV Swarm
Ninghui Li, Xiaokuan Zhang, Weike Feng, Fan Lv, Bowen Dai · Journal of Physics Conference Series · 2025
Abstract Unmanned aerial vehicle (UAV) swarms are highlighted by researchers and widely used in contemporary life and modern warfare owing to the advantageous small size, low cost, convenience to use, low requirements for the combat environment, strong battlefield survivability. Specifically, UAV swarms can carry out missions in complex and harsh situations with the least cost, such as monitoring, surveillance, reconnaissance, precise attacking. On this basis, resisting UAV swarms becomes more and more attractive but challenging. As a mainstream method, radar detection is vital in the anti-UAV field. However, radar cannot distinguish the UAVs in the same swarm because their disparities are too small in the domain of range, Doppler and angle. In view of that, we develop a multiple signal classification (MUSIC) algorithm to detect and locate the UAVs well. The foundation of the applied MUSIC is that the received data contains a phase term like the twiddle factor, so corresponding steering vectors in the Doppler and angle dimensions can be applicable. Extensive simulation results confirm that the used MUSIC-based algorithm is useful and excellent for detection and localization of UAV swarms.