Intelligent Penetration Route Planning For Unmanned Aerial Vehicle Based On Improved Dynamically Coded Particle Swarm Optimization

In the modern military field,it is the key to finish the precision strike mission by fine planning flight route of the unmanned aerial vehicles and then to achieve the high penetration capability of vehicles.Different from other areas of UAV,the stealth penetration field of UAV involves more complex constraints and the penetration problem itself involves more restrictions,so it poses a great challenge to the research of route planning algorithm.In this paper,an intelligent route planning method for UAV penetration based on improved dynamic coded particle swarm is proposed according to practical application requirements.Experiments show that the method is effective and has unique advantages.Firstly,this paper analyzes the kinematics model of the unmanned aerial vehicle,the dynamic RCS characteristics of the unmanned aerial vehicle,as well as the calculation method of the penetration probability of the UAV,proposes a penetration strategy based on the dynamic RCS characteristics of unmanned aerial vehicle,and the flight environment,the constraint region and the constraint conditions of the UAV are modeled and analyzed.Aiming at the low flexibility problem of the traditional particle swarm optimization(PSO)algorithm in solving route planning problems due to the fixed particle dimension,a intelligent penetration route planning algorithm for UAV based on improved dynamically coded particle swarm is proposed,in this method,the dimension of particles can be dynamically changed as needed: when the original particle fail,it can be "mutated" and increase the dimension of the particle,to ensure the effectiveness of the particles;When the particle is redundant,the dimension of the particle can be reduced dynamically.Combining with the idea of DTW algorithm,the problem of particle alignment adjustment under different particle dimensions is solved,and the updating strategy of each component with different particle dimensions is formulated.The method is applied to the stealth penetration field of UAV for route planning by combining this method with the dynamic RCS characteristics of UAV.Finally,multiple sets of comparative experiments were designed in a variety of scenarios which show the advantages of the intelligent penetration path planning method based on the improved dynamic coded particle swarm Optimization(IDCPSO):(1)Compared with the traditional particle swarm planning method,it can dynamically adjust the particle dimension and find the global optimal solution faster.(2)Taking advantage of the dynamic RCS characteristics of the aircraft,compared with the traditional planning algorithm that fixed the RCS,it can plan a route with a shorter route length and high penetration capability.(3)Compared with the existing dynamic coded particle swarm optimization(DCPSO),Sparse A* Search(SAS)algorithm and directional coding based genetic algorithm,the advantages of the proposed method compared with other methods are highlighted.(4)The algorithm has strong anti-interference ability,which can reasonably bypass the no-fly zone while avoiding radar detection,and plan the flight route that meets the penetration requirements.