Abstract: 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 an air-defense firepower network for the OODA operation process is proposed. Focusing on the OODA operation process, an air-defense system-of-systems framework composed of an intelligence network, command network, and firepower network is constructed. Based on this framework, the intelligent construction of a firepower network, which is the key to the success or failure of the battlefield, is solved. The process of intercepting the weapon damage 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 algorithm is optimized to improve the model accuracy and reduce the training time. Taking a joint regional operation scenario of air-defense and antimissile missiles as an example, the evaluation and verification of the proposed algorithm is carried out. The results show that compared with the rule-based and heuristic algorithm, this method can quickly and accurately generate the design scheme of air-defense firepower network, especially in terms of computational efficiency and operational cost in the same large-scale combat scenario, which provides the basis for the construction of the kill network in the whole process of the operation system-of-systems.

Key words: air-defense firepower network, improved PPO algorithm, OODA operation process, battlefield situation, end-to-end training