智能寻的制导律研究综述

doi:10.12305/j.issn.1001-506X.2025.12.24

Abstract

Abstract:

This paper summarizes the current state of intelligent seeking guidance laws, analyzing practical issues in traditional guidance methods, such as uncertainty in coefficient selection and inaccurate estimates of remaining flight time. It reviews research on single-missile intelligent guidance laws based on deep learning, reinforcement learning, and transfer learning, as well as multi-missile cooperative intelligent guidance laws. This paper discusses and anticipates future research directions for intelligent guidance laws. Emphasis is placed on in-depth research in respects of the necessity of incorporating intelligent methodologies, ensuring the determinacy of neural network designs, improving the reliability of data-driven approaches, addressing the complexities of multi-agent cooperative guidance, and advancing key technologies for autonomous intelligent guidance, to provide insights for future intelligent guidance method development.

Key words: intelligent guidance law, neural network, reinforcement learning, sliding mode control, transfer learning, cooperative guidance

CLC Number:

V 448.2

Jin TANG, Yishu WANG, Yangang LIANG, Haojian LI, Kebo LI. Research review of intelligent homing guidance law[J]. Systems Engineering and Electronics, 2025, 47(12): 4117-4129.

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Table 1

Main algorithm of DRL"

算法类别	算法名称（时间）	主要改进及其优势	适用场景	制导领域的应用
基于值函数	DQN（2013）	采用经验回放机制和两个Q网络，训练稳定；可结合卷积神经网络	高维状态空间，离散动作空间	文献[50]，利用DQN学习制导指令
	Double DQN （2016）	解耦目标Q值计算评估和动作选择，解决Q值过度估计问题	高维状态空间，离散动作空间， DQN过估计时使用	文献[62]，利用Double DQN 学习制导指令
	Dueling DQN （2016）	引入价值函数和优势函数优化网络结构，强化学习算法更好结合	高维状态空间，离散动作空间，状态空间变化大时使用	—
	D3QN（2018）	现有改进基础上再引入优先经验回放机制，训练稳定且收敛速度快	高维状态空间，离散动作空间	—
	Rainbow DQN （2018）	整合6种改进形式，训练效果明显提升，适用于多种场景	高维状态空间，离散动作空间，需高性能的DQN变体时使用	—
基于策略梯度	DDPG（2015）	双网络基础上引入价值网络和策略网络共4个，解决连续控制问题	连续动作空间，复杂控制问题，需要确定性策略时使用	文献[50, 63, 71, 72]，基于 DDPG学习智能制导策略
	TRPO（2015）	引入优势函数保证策略连续优势迭代，提高数据采样效率	连续动作空间，复杂控制问题，需要稳定训练时使用	文献[66]，利用TRPO将运动状态以端对端的方式映射制导指令
	A3C（2016）	通过多个智能体并行探索训练，缩短训练时间	离散或连续动作空间，大规模问题，需分布式训练时使用	文献[56]，利用飞行状态直接映射为导航比，获得新制导方法
	PPO（2017）	TRPO基础上限制新旧策略更新幅度，提升数据利用率，降低计算量	连续动作空间，复杂控制问题，需高效稳定策略优化时使用	文献[58, 59, 68, 69, 70, 80]，基于PPO学习制导参数或制导指令
	DPPO（2017）	基于PPO可多线程并行训练，避免参数震荡现象	连续动作空间，复杂控制问题，适合多线程并行训练	—
	TD3（2018）	采用两套网络估计不同的Q值，避免Q值的过高估计	连续动作空间，复杂控制问题， DDPG过估计问题时使用	文献[65, 81, 82]基于TD3学习制导指令，提出新制导方法

Table 1

Table 2

Main research and application of intelligent homing guidance method"

具体寻的制导问题	参考文献	采用方法	主要工作和改进	改进制导律的效果
导弹剩余飞行时间估计问题	文献[21−22]	DNN、残差神经网络	预测模块引入神经网络模型估计剩余飞行时间	提高剩余飞行时间预测精度实现时间角度控制制导
	文献[23−24]	ANN、反向传播（back propagation, BP）神经网络	采用ANN或BP网络建立剩余飞行时间映射网络	降低能量消耗提升控制精度
	文献[27]	Fisher融合算法	基于数据驱动在线估计脱靶量和剩余飞行时间	提升计算效率和估计精度
滑模制导的参数估计和抖振问题	文献[34−36]	模糊RBF神经网络	神经网络对滑模变结构项的增益进行在线估计	对变结构项的自适应调节同时减小系统抖振
	文献[37−39]	RBF神经网络	利用RBF网络估计目标运动信息和变结构函数项	使制导律自适应参数变化并增强系统的鲁棒性
	文献[42]	RBF神经网络	RBF网络引入二阶滑模面在线逼近有界不确定项	进一步提高制导精度
最优控制问题	文献[48]	前馈神经网络	前馈神经网络建立飞行状态到最优指令的映射关系	实现非线性最优制导指令的毫秒量级实时生成
最优控制问题	文献[49]	DNN和偏置PN	DNN结合偏置PN设计最优终端制导方案	动态非线性情况下能平衡最优性、实时性和冲击角约束
PN系数选择问题	文献[53−55]	Q-learning算法	基于Q-learning学习PN系数	自适应调整PN的导航比以获得最佳的制导策略
PN系数选择问题	文献[56]	SAC算法	基于SAC算法智能调参的自适应PN律	获得考虑脱靶量和能量损耗的制导参数决策系统
最优制导律的参数估计问题	文献[58−59]	PPO算法	基于PPO对最优制导律中的制导参数进行学习	有效降低燃料消耗并且具有较好的制导精度
拦截机动目标问题	文献[62−63, 65]	DQN、DDPG和TD3算法	基于DRL算法直接学习制导加速度	相比于传统的TPN具有更好的制导精度
拦截机动目标问题	文献[68−70]	PPO算法和元学习技术	基于强化学习训练端到端无模型的制导策略	有效应用于大气层外机动目标的拦截制导
监督学习制导算法迁移问题	文献[77−78]	域对抗神经网络	基于神经网络学习不同任务场景共享底层信息	实现不同任务之间的制导方法快速迁移
多弹协同制导问题	文献[81−82]	TD3算法	利用TD3算法学习拦截场景下多飞行器协同策略	有效满足协同拦截和脱靶量的要求
多弹协同制导问题	文献[83]	多智能体深度强化学习算法	分布式多智能体深度强化学习用于协同制导控制	能够扩展到其他具有更多集群和目标的协同场景

Table 2

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Research review of intelligent homing guidance law

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