Intelligent Trajectory Planning Algorithm for Unmanned Aerial Vehicles Based on Track Deception Against Radar Networks

This paper investigates an intelligent trajectory planning algorithm for unmanned aerial vehicles (UAVs) that utilizes track deception techniques against radar networks. The main objective of the algorithm is to minimize the flight distance of UAVs while generating a coherent phantom track to deceive radar networks. Firstly, we analyze the geometric coupling relationship among the false target, the UAV, and the radar, deriving the motion control equations for UAV trajectory planning. Subsequently, we mathematically formulate the problem of intelligent trajectory planning for UAVs based on this coupling relationship. The optimization model aims to reduce the flight distance of the UAVs as much as possible, considering the strict dynamic constraints of UAV platforms. Furthermore, by leveraging the pre-designed phantom track and prior knowledge of radar network locations, we employ the particle swarm optimization (PSO) method to solve the resulting optimization problem. Finally, through simulation results, we validate the effectiveness and feasibility of the proposed algorithm.