Research On Path Planning Of UAV Penetration Based On Swarm Intelligence Algorithm

With the rapid development of modern cognitive confrontation system,UAV intelligent penetration technology has risen rapidly,and has made significant achievements in the field of radar confrontation,and also promoted the development of modern cognitive electronic warfare.Path planning of UAV is the key of penetration technology.Aiming at the known and unknown threats in the penetration environment,it is necessary to study the optimal path planning method in different environments to ensure that the UAV can avoid threats safely and efficiently,and then reach the target point quickly to complete the task.This paper mainly studies the path planning of UAV in penetration environment from the following aspects:(1)In the scenario of cooperative jamming confrontation,the jamming resource allocation model is built based on the suppression jamming mode and multi-aircraft cooperative jamming mode.The artificial bee colony algorithm is used to solve the suppression jamming resource scheme,improving the superiority of the jamming resource allocation scheme and realizing the search of the optimal scheme,which creates a safe penetration airspace for the penetration UAV.(2)In certain the threat environment,aiming at the problem that artificial bee colony algorithm is easy to fall into local optimum and slow convergence speed in path planning,the cuckoo search-preponderance artificial bee colony algorithm is proposed to plan initial path for UAV.On the basis of suppressing the interference environment,a digital flight map with basic terrain,mountain peaks and radar threats is built,and the path cost model is established based on the self-mechanism constraints of the UAV during flight.Combining individual selection mechanism and Levy flight mechanism,a cuckoo Search-preponderance artificial bee colony algorithm is proposed to improve the accuracy of initial path planning under the known static threat environment.(3)In uncertain static threat environment,aiming at the problem of low real-time performance of initial path planning algorithm,the guided cuckoo Search-preponderance artificial bee colony algorithm is proposed to realize path re-planning for UAV.According to the external environment information perceived by the UAV,the unknown static threat environment is built,and the adaptive local search mechanism is introduced to balance the exploration ability and development ability of the algorithm,to improve the reliability of path re-planning when encountering uncertain static during flight.(4)In uncertain dynamic threat environment,aiming at the problem that the intelligent algorithm cannot make real-time online path planning and the dynamic window algorithm has low success rate when planning the path,the adaptive dynamic window algorithm is proposed to realize online path re-planning for UAV.According to the relevant information of the dynamic threat detected by the UAV,based on the Kalman filtering algorithm,the rolling window is used to predict the dynamic threat path.Based on the UAV kinematics theory,combined with the rolling window algorithm and the dynamic window algorithm,the adaptive dynamic window algorithm is used to extend the window in unit time to the prediction stage,and the average optimal path under multiple dynamic windows is solved as the path of the UAV in the next stage until the end point,which improves the effectiveness and safety of path replanning when the flight encounters uncertain dynamic burst threats..