智能寻的制导律研究综述

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

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (12): 4117-4129.doi: 10.12305/j.issn.1001-506X.2025.12.24

• 制导、导航与控制 • 上一篇

智能寻的制导律研究综述

唐进¹^,², 王一书¹^,², 梁彦刚¹^,², 李昊键¹^,², 黎克波¹^,²^,*

1. 国防科技大学空天科学学院，湖南长沙 410073
2. 太空系统运行与控制全国重点实验室，湖南长沙 410073

收稿日期:2024-12-17 修回日期:2025-02-10 出版日期:2025-05-23 发布日期:2025-05-23
通讯作者: 黎克波
作者简介:唐　进（1997—），男，博士研究生，主要研究方向为飞行器动力学与控制、深度强化学习
王一书（2001—），女，硕士研究生，主要研究方向为导弹制导与控制
梁彦刚（1979—），男，教授，博士，主要研究方向为飞行器总体设计与系统仿真、飞行器动力学与控制
李昊键（1999—），男，博士研究生，主要研究方向为导弹制导与控制
基金资助:
国家自然科学基金（12472359，U2441205）资助课题

Research review of intelligent homing guidance law

Jin TANG¹^,², Yishu WANG¹^,², Yangang LIANG¹^,², Haojian LI¹^,², Kebo LI¹^,²^,*

1. College of Aerospace Science and Engineering，National University of Defense Technology，Changsha 410073，China
2. State Key Laboratory of Space System Operation and Control，Changsha 410073，China

Received:2024-12-17 Revised:2025-02-10 Online:2025-05-23 Published:2025-05-23
Contact: Kebo LI

摘要/Abstract

摘要：

对当前智能寻的制导律的研究进行总结，分析了传统制导方法中存在的系数选择不确定、剩余飞行时间估计不准确等实际问题；综述了基于深度学习、强化学习、迁移学习的单弹智能制导律及多弹协同智能制导律的研究现状；对未来智能制导律的研究方向进行讨论和展望，强调智能制导律要从智能方法引入的必要性、神经网络设计的确定性、数据驱动的可靠性、多智能体协同制导的复杂性及自主智能制导的关键技术入手，开展更加深入的研究，为未来智能制导方法的设计提供思路。

关键词: 智能制导律, 神经网络, 强化学习, 滑模控制, 迁移学习, 协同制导

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

中图分类号:

V 448.2

唐进, 王一书, 梁彦刚, 李昊键, 黎克波. 智能寻的制导律研究综述[J]. 系统工程与电子技术, 2025, 47(12): 4117-4129.

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.

图/表 10

图1

图2

图3

图4

图5

图6

图7

图8

表1

DRL主要算法"

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

表1

表2

智能寻的制导方法主要研究和应用"

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

表2

参考文献 86

1	LU P. What is guidance[J]. Journal of Guidance, Control, and Dynamics, 2021, 44 (7): 1237- 1238.
2	周荻. 寻的导弹新型导引规律[M]. 北京: 国防工业出版社, 2002.
	ZHOU D. The new guidance law for seeking missiles[M]. Beijing: National Defense Industry Press, 2002.
3	ZARCHAN P. Tactical and strategic missile guidance: an introduction[M]. 7th ed. Reston, Va: American Institute of Aeronautics and Astronautics, 2019.
4	ZARCHAN P. Advanced tactical and strategic missile guidance[M]. 7th ed. Reston, Va: American Institute of Aeronautics and Astronautics, 2019.
5	GUELMAN M. The closed-form solution of true proportional navigation[J]. IEEE Trans. on Aerospace and Electronic Systems, 1976 (4): 472- 482.
6	GHOSE D. True proportional navigation with maneuvering target[J]. IEEE Trans. on Aerospace and Electronic Systems, 1994, 30 (1): 229- 237. doi: 10.1109/7.250423
7	白志会, 黎克波, 苏文山, 等. 现实真比例导引拦截任意机动目标捕获区域[J]. 航空学报, 2020, 41 (8): 338- 348.
	BAI Z H, LI K B, SU W S, et al. Realistic proportional guidance interception of any maneuvering target capture area[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41 (8): 338- 348.
8	CHEN X T, WANG J Z. Optimal control based guidance law to control both impact time and impact angle[J]. Aerospace Science and Technology, 2019, 84, 454- 463. doi: 10.1016/j.ast.2018.10.036
9	苏茂. 基于微分对策的攻防对抗末段制导方法研究[D]. 武汉: 华中科技大学, 2020.
	SU M. Research on terminal guidance method for offensive and defensive confrontation based on differential game theory[D]. Wuhan: Huazhong University of Science and Technology, 2020.
10	LI K B, CHEN L, BAI X Z, et al. Differential geometric modeling of guidance problem for interceptors[J]. Science China Technological Sciences, 2011, 41 (9): 1205- 1217.
11	刘远贺, 黎克波, 何绍溟, 等. 基于最优误差动力学的变速导弹飞行路程控制制导律[J]. 航空学报, 2023, 44 (7): 168- 181.
	LIU Y H, LI K B, HE S M, et al. Guidance law for variable speed missile flight path control based on optimal error dynamics[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (7): 168- 181.
12	LI K B, SHIN H S, TSOURDOS A. Capturability of a sliding mode guidance law with finite time convergence[J]. IEEE Trans. on Aerospace and Electronic Systems, 2020, 56 (3): 2312- 2325.
13	LU P. Introducing computational guidance and control[J]. Journal of Guidance, Control, and Dynamics, 2017, 40 (2): 241- 246.
14	周聪, 闫晓东, 唐硕, 等. 大气层内模型预测静态规划拦截中制导[J]. 航空学报, 2021, 42 (11): 241- 246.
	ZHOU C, YAN X D, TANG S, et al. In-atmosphere model prediction of static planning interception midguidance[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42 (11): 241- 246.
15	LU P. Deeper learning needed from machine learning[J]. Journal of Guidance, Control, and Dynamics, 2024, 47 (1): 1- 4.
16	WANG C Y, DONG W, WANG J, et al. Impact-angle-constrained cooperative guidance for salvo attack[J]. Journal of Guidance, Control, and Dynamics, 2022, 45 (4): 684- 703.
17	TEKIN R, ERER K S. Impact time and angle control against moving targets with look angle shaping[J]. Journal of Guidance, Control, and Dynamics, 2020, 43 (5): 1020- 1025.
18	JEON I S, LEE J I, TAHK M J. Impact-time-control guidance law for anti-ship missiles[J]. IEEE Trans. on Control Systems Technology, 2006, 14 (2): 260- 266. doi: 10.1109/TCST.2005.863655
19	LAM V. Time-to-go estimate for missile guidance[C]//Proc. of the AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005.
20	GUO Y H, LI X, ZHANG H J, et al. Data-driven method for impact time control based on proportional navigation guidance[J]. Journal of Guidance, Control, and Dynamics, 2020, 43 (5): 955- 966.
21	刘子超, 王江, 何绍溟. 基于深度学习的时间角度控制制导律[J]. 系统工程与电子技术, 2023, 45 (11): 3579- 3587.
	LIU Z C, WANG J, HE S M. Time angle control guidance law based on deep learning[J]. Systems Engineering and Electronics, 2023, 45 (11): 3579- 3587.
22	LIU Z C, WANG J, HE S M, et al. Learning prediction-correction guidance for impact time control[J]. Aerospace Science and Technology, 2021, 119, 107187. doi: 10.1016/j.ast.2021.107187
23	黄嘉, 常思江. 基于数据驱动的攻击时间和攻击角度控制导引律[J]. 系统工程与电子技术, 2022, 44 (10): 3213- 3220. doi: 10.12305/j.issn.1001-506X.2022.10.26
	HUANG J, CHANG S J. Data driven attack time and attack angle control guidance law[J]. Systems Engineering and Electronics, 2022, 44 (10): 3213- 3220. doi: 10.12305/j.issn.1001-506X.2022.10.26
24	黄嘉, 常思江, 陈琦, 等. 不依赖剩余飞行时间的数据驱动攻击时间控制导引律[J]. 兵工学报, 2023, 44 (8): 2299- 2309.
	HUANG J, CHANG S J, CHEN Q, et al. A data-driven attack time control guidance law that does not rely on remaining flight time[J]. Journal of Ordnance Engineering, 2023, 44 (8): 2299- 2309.
25	YANG Z Q, LIU X D, LIU H K. Impact time control guidance law with time-varying velocity based on deep reinforcement learning[J]. Aerospace Science and Technology, 2023, 142, 108603.
26	CHENG L, JIANG F H, WANG Z B, et al. Multi-constrained real-time entry guidance using deep neural networks[J]. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (1): 325- 340. doi: 10.1109/TAES.2020.3015321
27	LI H X, LI H J, CAI Y L. Efficient and accurate online estimation algorithm for zero-effort-miss and time-to-go based on data driven method[J]. Chinese Journal of Aeronautics, 2019, 32 (10): 2311- 2323. doi: 10.1016/j.cja.2019.05.013
28	HE S M, SHIN H S, TSOURDOS A. Computational missile guidance: a deep reinforcement learning approach[J]. Journal of Aerospace Information Systems, 2021, 18 (8): 571- 582. doi: 10.2514/1.I010970
29	WANG N Y, WANG X G, CUI N G, et al. Deep reinforcement learning-based impact time control guidance law with constraints on the field-of-view[J]. Aerospace Science and Technology, 2022, 128, 107765. doi: 10.1016/j.ast.2022.107765
30	刘子超, 王江, 何绍溟, 等. 基于预测校正的落角约束计算制导方法[J]. 航空学报, 2022, 43 (8): 325- 433.
	LIU Z C, WANG J, HE S M, et al. Guidance method based on prediction correction for landing angle constraint calculation[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (8): 325- 433.
31	张嘉文, 史金光, 刘佳佳, 等. 带落角约束的均值聚类神经网络滑模制导律研究[J]. 电光与控制, 2021, 28 (3): 46- 55. doi: 10.3969/j.issn.1671-637X.2021.03.009
	ZHANG J W, SHI J G, LIU J J, et al. Research on sliding mode guidance law of mean clustering neural network with drop angle constraint[J]. Electronics Optics & Control, 2021, 28 (3): 46- 55. doi: 10.3969/j.issn.1671-637X.2021.03.009
32	WANG Z K, FANG Y W, FU W X, et al. Cooperative guidance laws against highly maneuvering target with impact time and angle[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2022, 236 (5): 1006- 1016. doi: 10.1177/09544100211026081
33	LI Q C, ZHANG W S, HAN G, et al. Finite time convergent wavelet neural network sliding mode control guidance law with impact angle constraint[J]. International Journal of Automation and Computing, 2015, 12 (6): 588- 599. doi: 10.1007/s11633-015-0927-5
34	周德云, 杨振, 张堃. 基于模糊RBF网络的自适应变结构制导律设计[J]. 飞行力学, 2016, 34 (4): 54- 58.
	ZHOU D Y, YANG Z, ZHANG K. Design of adaptive variable structure guidance law based on fuzzy RBF network[J]. Flight Mechanics, 2016, 34 (4): 54- 58.
35	WANG Y, TANG S, SHANG W, et al. Adaptive fuzzy sliding mode guidance law considering available acceleration and autopilot dynamics[J]. International Journal of Aerospace Engineering, 2018, 2018: 6081801.
36	温先福, 李刚, 张兴, 等. 基于模糊神经网络的滑模变结构制导律的研究[J]. 弹道学报, 2014, 26 (4): 13- 18. doi: 10.3969/j.issn.1004-499X.2014.04.003
	WEN X F, LI G, ZHANG X, et al. Research on sliding mode variable structure guidance law based on fuzzy neural network[J]. Journal of Ballistics, 2014, 26 (4): 13- 18. doi: 10.3969/j.issn.1004-499X.2014.04.003
37	SHAO G H J, XU Z, WANG X M, et al. Adaptive three-dimensional guidance law based on neural dynamic surface control[C]//Proc. of the IEEE International Conference on Aircraft Utility Systems, 2016.
38	佟廷帅, 刘晓利, 张志勇, 等. 基于RBF神经网络增益调节的滑模制导律[J]. 兵器装备工程学报, 2019, 40 (12): 110- 114. doi: 10.11809/bqzbgcxb2019.12.022
	TONG T S, LIU X L, ZHANG Z Y, et al. Sliding mode guidance law based on RBF neural network gain adjustment[J]. Journal of Weapon Equipment Engineering, 2019, 40 (12): 110- 114. doi: 10.11809/bqzbgcxb2019.12.022
39	陈勃, 杨开红, 季海波. 一种基于RBF神经网络增益调节的三维鲁棒导引律设计[J]. 中国科学技术大学学报, 2015, 45 (4): 280- 285. doi: 10.3969/j.issn.0253-2778.2015.04.004
	CHEN B, YANG K H, JI H B. A three-dimensional robust guidance law design based on RBF neural network gain adjustment[J]. Journal of University of Science and Technology of China, 2015, 45 (4): 280- 285. doi: 10.3969/j.issn.0253-2778.2015.04.004
40	LAI C, WANG W H, LIU Z H, et al. Three-dimensional integrated guidance and control for terminal angle constrained attack against ground maneuvering target[J]. Proceedings of the Institution of Mechanical Engineers, 2019, 233 (7): 2393- 2412. doi: 10.1177/0954410018778988
41	LIU M Y, XIANG J M, ZHANG X J, et al. Nonsingular sliding mode control and RBF neural network based guidance law with impact angle[C]//Proc. of the OCEANS MTS/IEEE Charleston Conference, 2018.
42	LI S, JIANG X Q. RBF neural network based second-order sliding mode guidance for Mars entry under uncertainties[J]. Aerospace Science and Technology, 2015, 43, 226- 235. doi: 10.1016/j.ast.2015.03.006
43	KIM M, HONG D, PARK S. Deep neural network-based guidance law using supervised learning[J]. Applied Sciences, 2020, 10 (21): 7865.
44	WANG C H, CHEN C Y. Intelligent missile guidance by using adaptive recurrent neural networks[C]//Proc. of the IEEE 11th International Conference on Networking, Sensing and Control, 2014: 559−564.
45	RAHBAR N, BAHRAMI M. Synthesis of optimal feedback guidance law for homing missiles using neural networks[J]. Optimal Control Applications and Methods, 2000, 21 (3): 137- 142. doi: 10.1002/1099-1514(200005/06)21:3<137::AID-OCA668>3.0.CO;2-E
46	RAJAGOPALAN A, FARUQI F A. Intelligent missile guidance using artiﬁcial neural networks[J]. Artiﬁcial Intelligence Research, 2015, 4 (1): 3919- 3936.
47	方洋旺, 邓天博, 符文星. 智能制导律研究综述[J]. 无人系统技术, 2020, 3 (6): 36- 42.
	FANG Y W, DENG T B, FU W X. A Review of intelligent guidance law research[J]. Unmanned Systems Technology, 2020, 3 (6): 36- 42.
48	王坤, 段欣然, 陈征, 等. 过载和攻击时间约束下的非线性最优制导方法[J]. 系统工程与电子技术, 2024, 46 (2): 649- 657.
	WANG K, DUAN X R, CHEN Z, et al. Nonlinear optimal guidance method under overload and attack time constraints[J]. Systems Engineering and Electronics, 2024, 46 (2): 649- 657.
49	CHENG L, WANG H, GONG S P, et al. Neural-network-based nonlinear optimal terminal guidance with impact angle constraints[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (1): 819- 830.
50	白志会. 基于强化学习的拦截机动目标制导律研究[D]. 长沙: 国防科技大学, 2020.
	BAI Z H. Research on guidance law for intercepting mobile targets based on reinforcement learning[D]. Changsha: National University of Defense Technology, 2020.
51	李士勇, 章钱. 智能制导: 寻的导弹智能自适应导引律[M]. 哈尔滨: 哈尔滨工业大学出版社, 2011.
	LI S Y, ZHANG Q. Intelligent guidance: seeking missile intelligent adaptive guidance law[M]. Harbin: Harbin Institute of Technology Press, 2011.
52	LI K B, BAI A H, SHIN H S, et al. Capturability of 3D RTPN guidance law against true arbitrarily maneuvering target with maneuverability limitation[J]. Chinese Journal of Aeronautics, 2022, 35 (7): 75- 90.
53	张秦浩, 敖百强, 张秦雪. Q-learning强化学习制导律[J]. 系统工程与电子技术, 2020, 42 (2): 414- 419.
	ZHANG Q H, AO B Q, ZHANG Q X. Q-learning reinforcement learning guidance law[J]. Systems Engineering and Electronics, 2020, 42 (2): 414- 419.
54	王金强, 苏日新, 刘莉, 等. Q-learning强化学习协同拦截制导律[J]. 导航定位与授时, 2022, 9 (5): 84- 90.
	WANG J Q, SU R X, LIU L, et al. Q-learning reinforcement learning collaborative interception guidance law[J]. Navigation Positioning and Timing, 2022, 9 (5): 84- 90.
55	李庆波, 李芳, 董瑞星, 等. 利用强化学习开展比例导引律的导航比设计[J]. 兵工学报, 2022, 43 (12): 3040- 3047. doi: 10.12382/bgxb.2021.0631
	LI Q B, LI F, DONG R X, et al. Using reinforcement learning to develop navigation ratio design for proportional guidance law[J]. Journal of Ordnance Engineering, 2022, 43 (12): 3040- 3047. doi: 10.12382/bgxb.2021.0631
56	张豪, 朱建文, 李小平, 等. 针对高机动目标的深度强化学习智能拦截制导[J]. 北京航空航天大学学报, 2023, 51 (6): 2060- 2069.
	ZHANG H, ZHU J W, LI X P, et al. Deep reinforcement learning intelligent interception guidance for highly maneuverable targets[J]. Journal of Beihang University, 2023, 51 (6): 2060- 2069.
57	ZAVOLI A, FEDERICI L. Reinforcement learning for robust trajectory design of interplanetary missions[J]. Journal of Guidance, Control, and Dynamics, 2021, 44 (8): 1425- 1439.
58	YANG H W, HU J C, LI S, et al. Reinforcement learning-based robust guidance for asteroid approaching[J]. Journal of Guidance, Control, and Dynamics, 2024, 47 (10): 2058- 2072.
59	FURFARO R, SCORSOGLIO A, LINARES R, et al. Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach[J]. Acta Astronautica, 2020, 171, 156- 171. doi: 10.1016/j.actaastro.2020.02.051
60	GAUDET B, FURFARO R. Missile homing-phase guidance law design using reinforcement learning[C]//Proc. of the AIAA Guidance, Navigation, and Control Conference, 2012.
61	GAUDET B, FURFARO R, LINARES R. A guidance law for terminal phase exo-atmospheric interception against a maneuvering target using angle-only measurements optimized using reinforcement meta-learning[C]//Proc. of the AIAA Scitech 2020 Forum, 2020.
62	TANG J, BAI Z H, LIANG Y G, et al. An exoatmospheric homing guidance law based on deep Q network[J]. International Journal of Aerospace Engineering, 2022, 2022: 1544670.
63	LIANG Y G, TANG J, BAI Z H, et al. Homing guidance law design against maneuvering targets based on DDPG[J]. International Journal of Aerospace Engineering, 2023, 2023: 4188037.
64	SHALUMOV V. Cooperative online guide-launch-guide policy in a target-missile-defender engagement using deep reinforcement learning[J]. Aerospace Science and Technology, 2020, 104, 105996. doi: 10.1016/j.ast.2020.105996
65	邱潇颀, 高长生, 荆武兴. 拦截大气层内机动目标的深度强化学习制导律[J]. 宇航学报, 2022, 43 (5): 685- 695.
	QIU X Q, GAO C S, JING W X. Deep reinforcement learning guidance law for intercepting maneuvering targets in the atmosphere[J]. Journal of Astronautics, 2022, 43 (5): 685- 695.
66	陈文雪, 高长生, 荆武兴. 拦截机动目标的信赖域策略优化制导算法[J]. 航空学报, 2023, 44 (11): 282- 300.
	CHEN W X, GAO C S, JING W X. Trust region strategy optimization guidance algorithm for intercepting mobile targets[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (11): 282- 300.
67	梁晨, 王卫红, 赖超. 带攻击角度约束的深度强化元学习制导律[J]. 宇航学报, 2021, 42 (5): 611- 620.
	LIANG C, WANG W H, LAI C. Deep reinforcement meta learning guidance law with attack angle constraints[J]. Journal of Astronautics, 2021, 42 (5): 611- 620.
68	GAUDET B, FURFARO R, LINARES R, et al. Reinforcement meta-learning for interception of maneuvering exoatmospheric targets with parasitic attitude loop[J]. Journal of Spacecraft and Rockets, 2021, 58 (2): 386- 399. doi: 10.2514/1.A34841
69	GAUDET B, FURFARO R, LINARES R. Reinforcement learning for angle-only intercept guidance of maneuvering targets[J]. Aerospace Science and Technology, 2020, 99, 105746. doi: 10.1016/j.ast.2020.105746
70	GAUDET B, LINARES R, FURFARO R. Adaptive guidance and integrated navigation with reinforcement meta-learning[J]. Acta Astronautica, 2020, 169, 180- 190. doi: 10.1016/j.actaastro.2020.01.007
71	DU M J, PENG C, MA J J. Deep reinforcement learning based missile guidance law design for maneuvering target interception[C]//Proc. of the 40th Chinese Control Conference, 2021: 3733–3738.
72	WANG X, CAI Y L, FANG Y Z, et al. Intercept strategy for maneuvering target based on deep reinforcement learning[C]//Proc. of the 40th Chinese Control Conference, 2021: 3547–3552.
73	HONG D S, KIM M J, PARK S S. Study on reinforcement learning-based missile guidance law[J]. Applied Sciences, 2020, 10 (18): 6567. doi: 10.3390/app10186567
74	PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Trans. on Knowledge and Data Engineering, 2009, 22 (10): 1345- 1359.
75	LIU C, GRYLLIAS K. Simulation-driven domain adaptation for rolling element bearing fault diagnosis[J]. IEEE Trans. on Industrial Informatics, 2021, 1, 5760- 5770.
76	王育鹏, 吕帅帅, 杨宇, 等. 基于域自适应的复合材料结构损伤识别方法[J]. 航空学报, 2022, 43 (6): 184- 191.
	WANG Y P, LYU S S, YANG Y, et al. Damage identification method of composite structures based on domain adaptive[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43 (6): 184- 191.
77	罗皓文, 何绍溟, 金天宇, 等. 基于迁移学习的角度约束时间最短制导算法[J]. 航空学报, 2023, 44 (19): 242- 256.
	LUO H W, HE S M, JIN T Y, et al. A guidance algorithm based on transfer learning for minimizing time constraints from a perspective[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (19): 242- 256.
78	罗皓文, 何绍溟, 亢有为. 一种基于迁移学习的多任务制导算法[J]. 兵工学报, 2024, 45 (6): 1787- 1798.
	LUO H W, HE S M, KANG Y W. A multi-task guidance algorithm based on transfer learning[J]. Journal of Ordnance Engineering, 2024, 45 (6): 1787- 1798.
79	JIN T Y, HE S M. Ensemble transfer learning midcourse guidance algorithm for velocity maximization[J]. Journal of Aerospace Information Systems, 2023, 20 (4): 204- 215. doi: 10.2514/1.I011070
80	高树一, 林德福, 郑多, 等. 针对集群攻击的飞行器智能协同拦截策略[J]. 航空学报, 2023, 44 (18): 276- 291.
	GAO S Y, LIN D F, ZHENG D, et al. Intelligent collaborative interception strategy for aircraft targeting cluster attacks[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (18): 276- 291.
81	倪炜霖, 王永海, 徐聪, 等. 基于强化学习的高超飞行器协同博弈制导方法[J]. 航空学报, 2023, 44 (202): 55- 66.
	NI W L, WANG Y H, XU C, et al. Collaborative game guidance method for hypersonic aircraft based on reinforcement learning[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (202): 55- 66.
82	SU H, PENNG H, MEI X N, et al. Research on multi-aircraft time cooperative interception method based on TD3 algorithm[C]//Proc. of Chinese Intelligent Systems Conference, 2023, 1091: 653–665.
83	ZHOU W H, LI J, LIU Z H, et al. Improving multi-target cooperative tracking guidance for UAV swarms using multi-agent reinforcement learning[J]. Chinese Journal of Aeronautics, 2022, 35 (7): 100- 112. doi: 10.1016/j.cja.2021.09.008
84	LAN X J, CHEN J D, ZHAO Z J, et al. Cooperative guidance of multiple missiles: a hybrid coevolutionary approach[J]. IEEE Trans. on Control Systems Technology, 2024, 32 (1): 128- 142. doi: 10.1109/TCST.2023.3301141
85	NI W L, LIU J Q, LI Z, et al. Cooperative guidance strategy for active spacecraft protection from a homing interceptor via deep reinforcement learning[J]. Mathematics, 2023, 11, 4211. doi: 10.3390/math11194211
86	樊会涛, 张新朝. 精确制导武器智能化若干问题思考[J]. 航空兵器, 2024, 31 (2): 1- 7. doi: 10.12132/ISSN.1673-5048.2024.0033
	FAN H T, ZHANG X C. Think of several issues on precision guided weapons intelligentizing[J]. Aero Weaponry, 2024, 31 (2): 1- 7. doi: 10.12132/ISSN.1673-5048.2024.0033

[1]	黄庚, 李旦, 张建秋. 时变二次规划的鲁棒归零神经网络求解模型[J]. 系统工程与电子技术, 2025, 47(9): 2775-2784.
[2]	石文强, 雷迎科, 金虎, 滕飞. 基于自适应小波分解和轻量化网络架构的特定辐射源识别算法[J]. 系统工程与电子技术, 2025, 47(9): 2785-2796.
[3]	蒋明煜, 张顺生, 肖思瑶. 面向轻量级交叉注意力卷积网络的SAR目标识别[J]. 系统工程与电子技术, 2025, 47(9): 2853-2861.
[4]	姚鹏, 韩美玉, 王德川, 高志诚. 基于对抗进化强化学习的多无人艇追捕方法[J]. 系统工程与电子技术, 2025, 47(9): 2960-2970.
[5]	魏潇龙, 吴亚荣, 姚登凯, 赵顾颢. 基于深度强化学习的无人机空战机动分层决策算法[J]. 系统工程与电子技术, 2025, 47(9): 2993-3003.
[6]	张国庆, 徐轶晖, 李纪强, 张显库, 邱斌. 基于异步搜寻制导的机/船协同事件触发控制[J]. 系统工程与电子技术, 2025, 47(9): 3058-3065.
[7]	杨大鹏, 龚资浩, 王小也, 郭正玉, 罗德林. 基于多智能体强化学习的无人机协同截击机动决策研究[J]. 系统工程与电子技术, 2025, 47(9): 3076-3085.
[8]	陈文洁, 张浦, 史高翔, 刘林, 刘烜. 基于余弦校验关系的卷积神经网络LDPC码盲识别[J]. 系统工程与电子技术, 2025, 47(9): 3117-3125.
[9]	王奇, 潘宇宁, 郑峻峰, 王力. 考虑力学形变的相控阵天线波束控制实现策略[J]. 系统工程与电子技术, 2025, 47(8): 2421-2428.
[10]	杨知沐, 张绍杰, 张朝原, 王浩宇, 赵卯卯. 基于微分博弈的导弹避撞协同制导律设计[J]. 系统工程与电子技术, 2025, 47(8): 2667-2675.
[11]	张梦泽, 田黎育. 基于高分辨谱估计的雷达目标分类方法[J]. 系统工程与电子技术, 2025, 47(7): 2146-2153.
[12]	符小卫, 王辛夷, 乔哲. 基于APIQ算法的多无人机攻防对抗策略[J]. 系统工程与电子技术, 2025, 47(7): 2205-2215.
[13]	柳佳豪, 徐任杰, 孙茂桐, 姜九瑶, 李际超, 杨克巍. 基于强化学习的装备体系韧性优化方法[J]. 系统工程与电子技术, 2025, 47(7): 2216-2223.
[14]	朱运豆, 孙海权, 胡笑旋. 基于指针网络架构的多星协同成像任务规划方法[J]. 系统工程与电子技术, 2025, 47(7): 2246-2255.
[15]	符小卫, 王辛夷, 乔哲. 基于ASDDPG算法的多无人机对抗策略[J]. 系统工程与电子技术, 2025, 47(6): 1867-1879.

智能寻的制导律研究综述

Research review of intelligent homing guidance law

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 86

相关文章 15

编辑推荐

Metrics

本文评价