【摘要】 针对现代战争中载机与地空导弹之间的复杂攻防对抗问题，提出了一种基于神经网络的在线决策方法。该方法同时考虑载机机动生存与挂弹命中地面目标的双重约束，对载机机动策略与发射时机进行综合优化，以提高作战任务的成功率并满足实时决策需求。首先建立了反辐射导弹、地空导弹和载机的动力学模型，并构建了包含三者的攻防对抗场景模型，通过仿真分析了不同机动策略与发射时机对作战结果的影响，定义了操作时间来衡量任务成败；其次，采用遗传算法针对离散-连续混合参数问题进行离线优化，得到最优的载机机动策略和反辐射导弹发射时机，以此构建神经网络训练样本集，并搭建了神经网络模型进行训练和检验。最后，通过仿真算例验证了神经网络在线决策的有效性，结果表明该方法能够显著扩大反辐射导弹的优势区，提高任务成功率，且预测时间短，满足实时决策需求。更多还原

【Abstract】 Directed against complex attack and defense confrontation between carrier aircraft and surface-to-air missiles in modern warfare, this paper proposes an online decision-making method based on neural networks. In synthetic consideration of dual constraints of carrier aircraft maneuverability and target engagement, the aircraft’s maneuvering strategy and the launch timing are optimized, improving the success rate of combat missions and meeting the needs of real-time decision-making. Firstly, dynamics models with anti-radiation missile, surface-to-air missile, and carrier aircraft are established, and a model of having attack and defense confrontation scenario, including all these three, is constructed. Through the simulation, the impact of different maneuvering strategies and launch timing on combat outcomes is analyzed, and the operation time is defined to measure success or failure of mission. And then, a genetic algorithm is employed to optimize the discrete-continuous hybrid parameter problem offline, obtaining the optimal carrier aircraft maneuvering strategy and anti-radiation missile launch timing, and taking such as these to construct a neural network training dataset and building a neural network model for training and validation. Finally, the effectiveness of neural network-based online decision-making is verified through simulation examples. The results show that this method can significantly expand the dominance zone of anti-radiation missiles, increase the mission success rate, and provide rapid predictions, meeting the needs of real-time decision-making.更多还原