Cooperative Jamming Of UAV Formation To Netted Radar

Netted radar has received extensive attention due to its excellent ’ four-anti ’ performance and is the development direction of future radar operations.Compared with monostatic radar,netted radar has stronger target detection and target tracking capabilities.Traditional interference methods for monostatic radar have been difficult to compete with netted radar.In view of the electronic countermeasure advantages of netted radar,distributed jamming system is proposed to balance netted radar.In this context,this thesis studies the cooperative jamming problem of UAV formation against networked radar.The main work is as follows:1.Aiming at the problem of UAV formation control and navigation,a distributed UAV formation control and navigation method is studied.Firstly,the control model of multi-agent formation is established,and the control gain matrix is defined.Then the dynamic model of UAV is established,and the flight speed and turning speed limit of UAV are considered.Finally,the problem of solving the control gain matrix is transformed into a semi-definite programming problem to realize the flight control of each UAV in the UAV formation,so that it gradually converges to the desired formation and desired flight direction.This method provides a formation basis for subsequent multi-UAVs to efficiently perform cooperative jamming tasks.2.Aiming at the problem that the netted radar is easy to identify the track deception jamming,this thesis takes the geometrical dilution of precision(GDOP)of the netted radar positioning accuracy as the main optimization target to plan the false target track,and designs the false target track planning cost function including the netted radar GDOP to reduce the positioning accuracy of the netted radar to the false target track.Aiming at the problem that the false target path planning algorithm has too large optimization space,long algorithm time and poor path planning effect,a false target path planning algorithm based on node adaptive artificial bee colony(ABC)is proposed.The algorithm improves the traditional ABC path planning algorithm.Firstly,the value range of the track control node is adaptively adjusted according to the guidance vector and the terrain of the initial honey source feedback and the GDOP information of the netted radar,and the optimization space of the algorithm is reasonably reduced.Then,based on the natural selection idea of genetic algorithm,the high-quality honey source is selected as the first generation honey source of ABC algorithm in the optimized optimization space.Finally,the ABC algorithm is iterated to find the optimal false target track under the guidance of the false target track planning cost function including the network radar GDOP.The algorithm can improve the success rate and efficiency of UAV formation cooperative jamming network radar.3.Aiming at the interference decision-making problem of UAV formation cooperative jamming network radar,the UAV path planning is first studied as a multi-objective optimization problem,and the fast nondominated sorting genetic algorithm(NSGA2)is used to solve its Pareto optimal solution to shape the interference situation of UAV formation.Then,the interference resource scheduling model of UAV formation is established,and on the basis of optimizing the flight height of UAV formation,the genetic algorithm is used to solve the model to optimize the interference beam pointing of UAV formation under different interference situations and improve the safety of UAV formation.In addition,aiming at the problem that the traditional interference decision algorithm has no cognitive interference ability,and the ordinary reinforcement learning algorithm is difficult to combat the complex multi-function radar without correct prior knowledge,an interference decision algorithm based on two-layer reinforcement learning is proposed to help the jammer overcome the misleading of wrong prior knowledge,enhance the intelligent interference ability of UAV formation,and further improve the interference efficiency of UAV formation.