Receding Horizon Control Based Real-time Strategy for Missile Pursuit-evasion Game

This paper presents a solution to the online missile pursuit-evasion game (PEG) problem in a two-dimensional plane. A relative kinematic model of engagement is developed, and a game decision algorithm based on receding horizon control (RHC) is introduced. This algorithm converts the complex maneuver...

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Bibliographic Details
Published in2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA) pp. 408 - 415
Main Authors Li, Aoyi, Hu, Xiaoxiang, Ran, Jianpeng, Wei, Peng, Yuan, Xiaoqian
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.07.2024
Subjects
approximate Nash equilibrium
Approximation algorithms
Games
Iterative algorithms
iterative double optimization
Kinematics
Missiles
Nash equilibrium
Optimization
Planning
pursuit-evasion game
Real-time systems
receding horizon control
Systems simulation
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DOI10.1109/CCSSTA62096.2024.10691851

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Summary:This paper presents a solution to the online missile pursuit-evasion game (PEG) problem in a two-dimensional plane. A relative kinematic model of engagement is developed, and a game decision algorithm based on receding horizon control (RHC) is introduced. This algorithm converts the complex maneuvering decision problem into solving the optimal control sequence over a finite future time horizon. In addition, an algorithm based on iterative double optimization is proposed to approximate the Nash equilibrium solution for the PEG. This algorithm avoids the dimensionality catastrophe that occurs when searching for the global Nash equilibrium by seeking a local solution. Finally, simulations were conducted assuming opponents to be either uncontrolled uniform-speed players or intelligent maneuvering players. The strategy's performance was then compared across different scenarios. The results show that the proposed method successfully integrates local approximation and rolling optimization benefits, enabling real-time strategy planning for PEG with enhanced performance.
DOI:10.1109/CCSSTA62096.2024.10691851