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.
AAV Swarm Intelligent Interception Driven by Mission-Empowered Mean-Field Gate Recurrent Reinforcement Learning
IEEE Transactions on Aerospace and Electronic Systems ; 61 , 3 ; 6373-6389
01.06.2025
6334279 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BASE | 2023
|Large-scale UAV swarm path planning based on mean-field reinforcement learning
Elsevier | 2025
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