基于深度Q学习的组网雷达闪烁探测调度方法

doi:10.12305/j.issn.1001-506X.2025.05.07

Abstract

Abstract:

The netted radar scintillation detection system can improve the cooperative detection performance and survival rate of radar. It is an urgent problem to select a suitable radar cooperative detection startup and limit the startup exposure time of a single radar to adapt to the ever-changing environmental threats. In this regard, a netted radar scintillation detection scheduling method is presented based on deep Q-learning (DQL) reinforcement learning algorithm to limit the startup time of a single radar. Firstly, the threat degree model of the air jammer to the netted radar and the scintillation detection model of the netted radar to the air jammer are established. Then, the reinforcement learning reward function of the threat degree and the netted scintillation detection probability is proposed. Finally, the optimal scintillation startup decision scheduling scheme of the netted radar is obtained by using the proposed DQL algorithm. The simulation results show that the average benefit rate of the proposed DQL scheduling method is superior to random scheduling, artificial bee colony scheduling and double deep Q network(DDQN) scheduling methods, and the scheduling response time is less.

Key words: netted radar, scintillation detection, reinforcement learning, deep Q-learning (DQL), double deep Q network (DDQN

CLC Number:

TN973

Zhikang LIN, Longfei SHI, Jialei LIU, Jiazhi MA. Scintillation detection scheduling method of netted radar based on deep Q-learning[J]. Systems Engineering and Electronics, 2025, 47(5): 1443-1452.

Figures/Tables 15

Table 1

Fig.1

Table 2

Table 3

Fig.2

Fig.3

Table 4

Table 5

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 6

References 28

1	FARINA A. Electronic counter-countermeasures[M]. 3rd ed. SKOLNIK M, ed. New York: McGraw-Hill, 2008.
2	YANG Z P , YANG S N , ZHOU Q S , et al. A joint optimization algorithm for focused energy delivery in precision electronic warfare[J]. Defence Technology, 2022, 18 (4): 709- 721. doi: 10.1016/j.dt.2021.03.001
3	LI S X , LIU G Y , ZHANG K , et al. DRL-based joint path planning and jamming power allocation optimization for suppressing netted radar system[J]. IEEE Signal Processing Letters, 2023, 30 (4): 548- 552.
4	ZHAO Z , LI X L , ZHANG Z R , et al. Optimal placement method of netted MIMO radar nodes based on hybrid integration for surveillance applications[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (3): 3537- 3552. doi: 10.1109/TAES.2024.3368378
5	YAN J K , ZHANG T , MA L , et al. Deployment optimization for integrated search and tracking tasks in netted radar system based on Pareto theory[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (3): 3664- 3672. doi: 10.1109/TAES.2024.3367662
6	孙兵, 李龙骧, 罗景青. 协同侦察系统增加猝发探测功能的定位技术[J]. 航天电子对抗, 2016, 32 (3): 9- 12.
	SUN B , LI L X , LUO J Q . Location technology of synergy reconnaissance system adding instantaneous detection function[J]. Aerospace Electronic Warfare, 2016, 32 (3): 9- 12.
7	高石印, 石玮, 王钦, 等. 地对空雷达干扰机布阵与开机时序控制研究[J]. 空军预警学院学报, 2020, 34 (5): 346-350, 355.
	GAO S Y , SHI W , WANG Q , et al. Research on ground-to-air radar jammer embattling and jamming time sequence control[J]. Journal of Air Force Early Warning Academy, 2020, 34 (5): 346-350, 355.
8	陈兴凯, 韩壮志, 封吉平, 等. 基于跟踪精度的火控雷达网间歇开机控制策略[J]. 探测与控制学报, 2013, 35 (5): 74- 78.
	CHEN X K , HAN Z Z , FENG J P , et al. Intermittent control strategy of fire-control radar network based on tracking accuracy[J]. Journal of Detection & Control, 2013, 35 (5): 74- 78.
9	SMITH G E , CAMMENGA Z , MITCHELL A , et al. Experiments with cognitive radar[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31 (12): 34- 46. doi: 10.1109/MAES.2016.150215
10	MORCERF L A, KONTSON K R, CLEMA J K. A concept for the application of artificial intelligence technology to battlefield spectrum management[C]//Proc. of the IEEE Military Communications Conference-Crisis Communications, 1987: 161-166.
11	MOUSAVI S S, SCHUKAT M, HOWLEY E. Deep reinforcement learning: an overview[C]//Proc. of the SAI Intelligent Systems Conference, 2016: 426-440.
12	SUTTON R S , BARTO A G . Reinforcement learning: an introduction[J]. Robotica, 1999, 17 (2): 229- 235.
13	GAO Y , CHEN S F , LU X . Research on reinforcement learning technology: a review[J]. Acta Automatica Sinica, 2004, 30 (1): 86- 100.
14	WANG Y H, ZHANG T X, XU L X, et al. Model-free reinforcement learning based multi-stage smart noise jamming[C]//Proc. of the IEEE Radar Conference, 2019.
15	SIDDESHA K , JAYARAMAIAH G V , SINGH C . A novel deep reinforcement learning scheme for task scheduling in cloud computing[J]. Cluster Computing, 2022, 25 (6): 4171- 4188. doi: 10.1007/s10586-022-03630-2
16	YOU C , LU J , FILEV D , et al. Advanced planning for auto-nomous vehicles using reinforcement learning and deep inverse reinforcement learning[J]. Robotics and Autonomous Systems, 2019, 114, 1- 18. doi: 10.1016/j.robot.2019.01.003
17	LI Y J , ZHU Y P , GAO M G . Design of cognitive radar jamming based on Q-learning algorithm[J]. Transactions of Beijing Institute of Technology, 2015, 35 (11): 1194- 1199.
18	TAYLOR G, WAGNER K, RADEMACHER P. Deep Q-network for radar task-scheduling problem[C]//Proc. of the IEEE Radar Conference, 2022.
19	ZHANG W X , ZHAO T , ZHAO Z K , et al. An intelligent strategy decision method for collaborative jamming based on hierarchical multi-agent reinforcement learning[J]. IEEE Trans. on Cognitive Communications and Networking, 2024, 10 (4): 1467- 1480. doi: 10.1109/TCCN.2024.3373640
20	ZHANG H W , XIE J W , ZHANG Z J , et al. Online task interleaving scheduling for the digital array radar[J]. AEU-International Journal of Electronics and Communications, 2017, 79, 250- 256.
21	YAN J K , PU W Q , DAI J H , et al. Resource allocation for search and track application in phased array radar based on Pareto bi-objective optimization[J]. IEEE Trans. on Vehicular Technology, 2019, 68 (4): 3487- 3499. doi: 10.1109/TVT.2019.2894960
22	邢怀玺, 邢清华. 雷达闪烁探测优化调度模型[J]. 北京航空航天大学学报, 2024, 50 (12): 3884- 3893.
	XING H X , XING Q H . An optimal scheduling model for scintillation detection of netted radars[J]. Journal of Beijing University of Aeronautics and Astronautics, 2024, 50 (12): 3884- 3893.
23	FENG J F , ZHANG Q , HU J H , et al. Dynamic assessment method of air target threat based on improved GIFSS[J]. Journal of Systems Engineering and Electronics, 2019, 30 (3): 525- 534. doi: 10.21629/JSEE.2019.03.10
24	苏冠霞, 马华强, 李祥, 等. 基于层次分析法的陆基预警雷达固有威胁评估[J]. 电子信息对抗技术, 2021, 36 (4): 87- 91.
	SU G X , MA H Q , LI X , et al. Inherent threat assessment of land-based early warning radar based on analytic hierarchy process[J]. Electronic Information Countermeasure Technology, 2021, 36 (4): 87- 91.
25	CHEN Y F , WU Y , CHEN N , et al. New approximate distributions for the generalized likelihood ratio test detection in passive radar[J]. IEEE Signal Processing Letters, 2019, 26 (5): 685- 689. doi: 10.1109/LSP.2019.2903632
26	RICHTER R, GOMES N A S. A-4 Skyhawk aircraft stealth capa-city against L-band radar based on dynamic target detection[C]// Proc. of the IEEE Radar Conference, 2020.
27	尹康银, 姜志敏, 冯亚军. 预警雷达工作模式分配两阶段优化策略[J]. 兵工学报, 2022, 43 (2): 328- 336.
	YIN K Y , JIANG Z M , FENG Y J . Two-stage optimization strategy of assignment for operating modes of early warning radar[J]. Acta Armamentarii, 2022, 43 (2): 328- 336.
28	JANG B , KIM M , HARERIMANA G , et al. Q-learning algorithms: a comprehensive classification and applications[J]. IEEE Access, 2019, 7, 133653- 133667. doi: 10.1109/ACCESS.2019.2941229

时间	时刻
时间	1	…	33	34	35	36	37	38	39	40
4	1	…	1	1	1	1	0	0	0	0
3	1	…	1	1	1	1	1	0	0	0
2	1	…	1	1	1	1	1	1	0	0
1	1	…	1	1	1	1	1	1	1	0

参数	数值
干扰总功率/kW	6.8
增益/dB	30
极化失配损失	0.5
干扰频段/GHz	2~18
系统损耗	0.15

参数	数值
发射功率/kW	1 200
发射增益/接收增益/dB	30/30
脉冲积累数	16
虚警概率	10^－6
扫描周期/s	10
波长/m	0.1
系统损耗	0.15
目标雷达散射截面/m²	1

方法	平均探测率/%	平均威胁度/%	平均效益率/%	时间损耗/s
随机调度	37.26	30.35	30.03	3
ABC调度	56.96	27.86	55.58	13
DDQN调度	66.02	34.56	59.06	872
DQL调度	72.23	25.56	71.37	12

[1]	Ziyi WANG, Xiongjun FU, Jian DONG, Cheng FENG. Optimization of radar collaborative anti-jamming strategies based on hierarchical multi-agent reinforcement learning [J]. Systems Engineering and Electronics, 2025, 47(4): 1108-1114.
[2]	Wei XIONG, Dong ZHANG, Zhi REN, Shuheng YANG. Research on intelligent decision-making methods for coordinated attack by manned aerial vehicles and unmanned aerial vehicles [J]. Systems Engineering and Electronics, 2025, 47(4): 1285-1299.
[3]	Peng MA, Rui JIANG, Bin WANG, Mengfei XU, Changbo HOU. Strategy reconstruction for resilience against intelligence jamming based on implicit opponent modeling [J]. Systems Engineering and Electronics, 2025, 47(4): 1355-1363.
[4]	Kaiqiang TANG, Huiqiao FU, Jiasheng LIU, Guizhou DENG, Chunlin CHEN. Hierarchical optimization research of constrained vehicle routing based on deep reinforcement learning [J]. Systems Engineering and Electronics, 2025, 47(3): 827-841.
[5]	Xiarong CHEN, Jichao LI, Gang CHEN, Peng LIU, Jiang JIANG. Portfolio of weapon system-of-systems based on heterogeneous information networks [J]. Systems Engineering and Electronics, 2025, 47(3): 855-861.
[6]	Ke FU, Hao CHEN, Yu WANG, Quan LIU, Jian HUANG. Uncertainty-based Bayesian policy reuse method [J]. Systems Engineering and Electronics, 2025, 47(2): 535-543.
[7]	Xiaolin LIU, Mengjiao GUO, Zhuo LI. Adaptive graph convolutional recurrent network prediction method for flight delay based on Dueling DQN optimization [J]. Systems Engineering and Electronics, 2025, 47(2): 568-579.
[8]	Xunliang YAN, Kuan WANG, Zijian ZHANG, Peichen WANG. Reentry guidance method based on LSTM-DDPG [J]. Systems Engineering and Electronics, 2025, 47(1): 268-279.
[9]	Tingyu ZHANG, Ying ZENG, Nan LI, Hongzhong HUANG. Spacecraft power-signal composite network optimization algorithm based on DRL [J]. Systems Engineering and Electronics, 2024, 46(9): 3060-3069.
[10]	Yuqi XIA, Yanyan HUANG, Qia CHEN. Path planning for unmanned vehicle reconnaissance based on deep Q-network [J]. Systems Engineering and Electronics, 2024, 46(9): 3070-3081.
[11]	Zhipeng YANG, Zihao CHEN, Chang ZENG, Song LIN, Jindi MAO, Kai ZHANG. Online route planning decision-making method of aircraft in complex environment [J]. Systems Engineering and Electronics, 2024, 46(9): 3166-3175.
[12]	Weiqi ZOU, Chaoyang NIU, Wei LIU, Yanyun WANG, Jiaqi ZHAN. Multi-syndrome jammers formation trajectory preplanning method for netted radar jamming task [J]. Systems Engineering and Electronics, 2024, 46(8): 2807-2819.
[13]	Lisha PENG, Yuxiang SUN, Yufan XUE, Xianzhong ZHOU. Intelligent decision-making technology for wargame by integrating three-way multiple attribute decision-making and SAC [J]. Systems Engineering and Electronics, 2024, 46(7): 2310-2322.
[14]	Hongda GUO, Jingtao LOU, Youchun XU, Peng YE, Yongle LI, Jinsheng CHEN. Event-triggered communication of multiple unmanned ground vehicles collaborative based on MADDPG [J]. Systems Engineering and Electronics, 2024, 46(7): 2525-2533.
[15]	Yaling ZHUO, Xiang LI, Lei ZUO, Juan HU. Power allocation algorithm with random data packet loss in netted radar [J]. Systems Engineering and Electronics, 2024, 46(6): 1957-1966.

Scintillation detection scheduling method of netted radar based on deep Q-learning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 28

Related Articles 15

Recommended Articles

Metrics

Comments

模式	雷达1	雷达2	雷达3	雷达4
1	1	0	0	0
2	0	1	0	0
3	0	0	1	0
4	0	0	0	1
5	1	1	0	0
6	1	0	1	0
7	1	0	0	1
8	0	1	1	0
9	0	1	0	1
10	0	0	1	1
11	1	1	1	0
12	1	1	0	1
13	1	0	1	1
14	0	1	1	1
15	1	1	1	1