Abstract：Given the great dynamic change in the modern air defense combat environment, the air defense missile weapon system requires real-time online target allocation. In this study, a real-time target allocation method for air defense missiles based on a genetic algorithm (GA) optimized back propagation (BP) neural network was proposed. Firstly, the optimization model of the air defense missile target allocation problem was established by employing the number of air defense missile weapons and damage probability, and the maximum damage effectiveness was set as the optimization goal. Then, the air defense missile target allocation framework based on the GA-BP neural network was constructed. The optimal weights and thresholds of the BP neural network were acquired using a genetic algorithm to optimize the BP neural network. The accurate and efficient allocation of threat targets to the current air defense missile was achieved from neural network prediction. Finally, the optimized neural network was applied for simulation analysis, allowing real-time target allocation under the battlefield situation, and verifying the effectiveness and practicability of the proposed method.
孙栋一, 蒲宇亭, 章建榜. 基于GA-BP神经网络的防空导弹实时目标分配方法[J]. 空天防御, 2025, 8(1): 62-70.
SUN Dongyi, PU Yuting, ZHANG Jianbang. Target Assignment Method of Air Defense Missile Based on GA-BP Neural Network. Air & Space Defense, 2025, 8(1): 62-70.
