Stealth penetration path planning for stealth unmanned aerial vehicle based on improved rapidly-exploring-random-tree

Because the air-defense radar net is increasingly dense in the modern warfare, a stealth penetration path planning scheme based on improved rapidly-exploring-random-tree (RRT) is proposed to address the flight survivability problem of the stealth unmanned aerial vehicles (UAV). Firstly, two crucial characters of the stealth penetration path planning, the dynamic radar cross section (RCS) of the aircraft and the radar detection criterion, are analyzed and modeled. Secondly, an improved RRT is proposed to solve the path planning problem of the stealth UAV which has not been well handled by existing methods. When the improved algorithm generates a new vertex, the RCS variation according to different attitude angles is taken into consideration. Lastly, the feasibility of the new vertex is estimated through the average value of the instantaneous detection probability of several vertexes around it. This value is calculated with the receding horizon control strategy. The simulation result and the comparison study show that the two characters of the penetration path planning can be well handled by the proposed algorithm, and a better penetration route can be generated efficiently in complex scenarios.