Unmanned aerial vehicle cluster target defense method based on deep reinforcement learning

Abstract

The invention provides an unmanned aerial vehicle cluster target defense method based on deep reinforcement learning. In the training stage, initial position information and behavior information of anattacking target are obtained, a training neural network of deep reinforcement learning is established, the neural network is trained, in the execution stage, an unmanned aerial vehicle cluster stateand a target state are obtained and input into the trained deep neural network, and an output result is judged. According to the method, the state space and the behavior space of the unmanned aerialvehicle clustering task are expanded, the task-oriented unmanned aerial vehicle clustering unified decision network is constructed, and unified command and control of the decision network on the uncertain number of unmanned aerial vehicles are realized.

Description

technical field [0001] The invention relates to the fields of machine learning, path planning and target confrontation, in particular to an unmanned swarm target defense method. Background technique [0002] Nowadays, the research on UAV swarms has attracted more and more attention from everyone, and has made certain progress in the direction of UAV swarm mission decision-making, information interaction and information fusion between UAVs, collaborative detection, path planning, and interaction methods. As a result of the research, among the mission sequences of many UAV swarms, the defense mission is the most important thing that cannot be bypassed. In the battlefield environment, it is often necessary to defend one's own important strategic goals and prevent enemy targets from attacking. Therefore, deploying drones around the base to prevent possible enemy attacks will become a drone cluster. one of the important tasks. [0003] The current research on the behavior contr...

AI Technical Summary

Problems solved by technology

Judging from the achievements of the coordinated missions of UAV clusters realized in China, my country's UAV clusters have more research on how to effectively prevent collisions when UAV clusters perform tasks collaboratively, and the payload of UAVs and its execution are more complex. High-level tasks, such as: coordinated detection, coordinated attack, coordinated defense and other task-level related research are still relatively few

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