Drone detection method based on K-Medoids to extract channel state characteristics - SONG Lingyu; PAN Peng; LIU Tianle (Dianxin kexue)
Effective management of low-altitude targets is key to promot the development of the low-altitude economy. In urban environments, strong clutter and building occlusion make it difficult for traditional radar detection methods to effectively monitor low-speed drones. Based on this, a new approach of drone detection was proposed, which involved identifying changes in channel state characteristics to determine whether a drone was presented in a specified area. The core of this method lied in utilizing the already widely deployed mobile base stations and other external radiation sources in cities, capturing the impact of drone presence on the number of multipath channel paths by using the K-Medoids clustering algorithm, to achieve drone perception. This method did not require the construction of an accurate reference signal nor the use of Doppler systems to suppress strong clutter. Simulation results show that the proposed method can achieve detection probabilities of over 80% within a range of 1 square kilometer, and the detection probability can reach about 90% as the range decreases. Therefore, it is capable of effectively detecting low-altitude, slow-moving drones in urban scenarios.