Intercepting a saturation attack from a loitering munition (LM) swarm with a swarm of autonomous aerial vehicles (AAVs) is a complex challenge. In this study, we developed a specific intercept mission simulation environment and proposed an approach based on the gate recurrent unit (GRU) mean-field deep deterministic policy gradient algorithm to tackle this issue. Based on the battle situation of both sides, we constructed a target assignment model for the AAV swarm based on the extensible Hungarian algorithm. To address the operational characteristics of large-scale AAV swarm interception missions, a partially observable mean-field game theory was integrated to modify the DDPG algorithm, with a GRU incorporated to predict the movements of incoming LMs accurately. The algorithm was trained under a “centralized training, distributed execution” framework. Simulation results demonstrate that this approach significantly enhances the interception success rate of the AAV swarm against large-scale LMs.


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    Title :

    AAV Swarm Intelligent Interception Driven by Mission-Empowered Mean-Field Gate Recurrent Reinforcement Learning


    Contributors:
    Zhang, Yaozhong (author) / Ding, Meiyan (author) / Du, Yu (author) / Peng, Fulun (author) / Wang, Jing (author) / Yang, Qiming (author)


    Publication date :

    2025-06-01


    Size :

    6334279 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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