Improvement of UAV Tracking Technology in Future 6G Complex Environment Based on GM-PHD Filter

Unmanned aerial vehicles (UAVs) will become an indispensable part of future sixth-generation (6G)-based mobile networks that can provide flexible deposition, strong adaptability, and high service quality. Under the guarantee blockchain, UAVs efficient communication or computing services for ground intelligence devices promote development wireless communication. However, as number increases, issues regarding UAV path planning, handling emergencies, intrusion illegal UAVs, etc., need to be addressed. This paper proposes improved Gaussian mixture probability hypothesis density (GM-PHD) filter based on machine learning target tracking recognition non-cooperative swarms. Simulation results demonstrate effectively suppress clutter interference in complex environments improve performance multi-target trajectory compared with traditional GM-PHD filter.