针对导弹打击机动目标的问题,提出了一种基于组合导引律的粒子群优化算法以应对战场环境中的机动目标打击问题。首先对三维弹目运动关系进行分析,设计了一种包含角度和时间偏置项的比例导引律,旨在有效抑制终端角度变化,同时确保打击时间的精确控制。为实现最优导引律参数的求解,研究构建了以打击角、打击时间、法向加速度和脱靶量等约束条件为基础的代价函数。采用标准粒子群算法进行自适应寻优,通过迭代计算确定使代价函数最小化的最优导引律参数,进而得到满足约束条件的最优弹道。为验证算法的有效性,设置了机动目标场景,并设计了与原始比例导引律的对比仿真实验以及多种打击时间测试实验。实验结果表明,相较于传统比例导引律,基于粒子群优化的组合导引律算法在应对机动目标时能够生成满足打击时间要求及其他约束条件的弹道,并在不同打击时间设定下均展现出良好的打击效果,具有广阔的应用前景。

The proportional guidance law is a classical guidance method that offers significant benefits, such as excellent ballistics and minimal data requirements. However, this method faces challenges in meeting requirements related to maneuvering targets and inter-bomb coordination time constraints, due to its ballistic characteristics. Particle swarm optimization (PSO) is a widely used optimization strategy that can search for parameter-optimal solutions within constraints. This strategy follows established design steps. In this article, the proportional guidance law is enhanced by the correction of strike angle and strike time, resulting in a combined guidance law. By using constraint functions for striking angle, overload, off-target amount, and striking time, the PSO technique is employed to optimize the parameters of the combined guidance law, thereby achieving an effective strike. The experimental results show that this guidance law design and the intelligent parameter selection strategy can enable a missile to meet relevant requirements with a strike time error of less than 1%. Compared to traditional empirical parameter selection methods, intelligent parameter selection yields better ballistic characteristics and reduces the effect of target maneuvering.