National University of Defense Technology
Field fund of equipment development department
Aiming at the problem that the single type of intercepting equipment of the current anti-drone system can not effectively suppress the drone, a new compound anti-drone method is constructed by using multiple types of intercepting equipment and adopting the principle of minimum distance shooting to break through the task assignment problem of multiple types of intercepting equipment. Aiming at the problem of slow solution speed of traditional multi-objective optimization algorithm, difficult to adjust the parameters of intelligent algorithm and unable to effectively balance the global search and local optimization, this paper proposes a task assignment model of multi-type interception equipment compound anti-drone based on deep Q network (DQN). In order to improve the convergence speed and learning efficiency of the algorithm, this method does not use the state of the next time to predict the Q value, but uses the state of the current time to predict the Q value, while eliminating the influence of over estimation of Q value in the training process. In the model training process, the corresponding agents are trained for each interception device in a one-to-one interception mode. In actual use, the agents that meet the interception conditions are autonomously intercepted according to the minimum distance shooting principle. The simulation environment of task assignment of anti-drone system is constructed by taking the open area around a domestic airport runway as the protection object. The simulation results verify the effectiveness of this method. At the same time, compared with the DQN and Double DQN methods, the improved DQN algorithm training agent performance is more accurate, and the convergence of the algorithm and the performance of the solution are more excellent. The method in this paper provides new ideas for the anti-drones problem.