The loitering munition is a weapon system that integrates drone technology with ammunition technology, capable of conducting extended autonomous cruising, reconnaissance, identification, and fire strike missions. It finds extensive applications in modern unmanned warfare. The main interception method for such unmanned aerial attack weapons is missile interception. However, there is a large cost disparity between missiles and loitering munitions. To achieve low-cost interception, utilizing unmanned aerial vehicles for interception has become a major research direction. In order to enhance interception effectiveness and achieve intelligent interception, this paper constructs a three-dimensional interception scenario using loitering munition as the interception method. Employing the Deep Q-Network (DQN) algorithm, it trains the intelligent decision-making capabilities of loitering munition. The paper designs neural networks and reward functions to train the maneuver strategic decision model and tests it against scenarios involving various enemy evasion strategies. The results indicate that the trained model possesses interception capability, enabling it to adjust its maneuvering during flight to track and intercept targets.
Loitering Munition Interception Decision-Making Technology Based on Deep Reinforcement Learning
Lect. Notes Electrical Eng.
International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024
04.03.2025
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Optimization Design of Small Loitering Munition Electric-Powered Propulsion System
Springer Verlag | 2025
|Sensitivity Analysis of CFRP Monocoque Structural Response Subjected to Loitering Munition Explosion
Springer Verlag | 2025
|Defence - Rivals unveil loitering munition concepts . Saudi and Oman eye Paveway IV
Online Contents | 2005
Aerodynamic Simulation of a Large Aspect Ratio Loitering Munition with Morphing Wings and Tails
Springer Verlag | 2023
|