End-to-end Intelligent Construction Algorithm of Air-defense Firepower Network for OODA Operation Process
Aiming at the problems of a large number of targets,difficult equipment coordination,and poor system response in an air-defense battlefield environment,an end-to-end intelligent construction algorithm of air-defense firepower network for the observe-orient-decide-act(OODA)operation process is proposed.An air-defense system-of-systems framework composed of an intelligence network,command network,and firepower network is constructed for the OODA operation process.Based on this framework,the intelligent construction of firepower network,which is the key to the success or failure on a battlefield,is solved.The process of intercepting the weapon attacking a target is modeled as a Markov decision process,and the corresponding state space,action space,and reward strategy are given.On this basis,the standard end-to-end proximal policy optimization(PPO)algorithm is optimized to improve the model accuracy and reduce the training time.The proposed algorithm is evaluated and verified by taking a joint regional operation scenario of air-defense and antimissile missiles as an example.The results show that the proposed algorithm can quickly and accurately generate the design scheme of air-defense firepower network compared with the rule-based and heuristic algorithm.Especially in terms of computational efficiency and operational cost in the same large-scale combat scenario,it provides the basis for the construction of a kill network in the whole process of the operation system-of-systems.
air-defense firepower networkimproved PPO algorithmOODA operation processbattlefield situationend-to-end training
万方数据
维普
