基于双胞循环神经网络的雷达捷变频行为识别

doi:10.12305/j.issn.1001-506X.2024.03.15

摘要/Abstract

摘要：

雷达程控捷变频行为具有一定的抗窄带瞄准式干扰能力, 同时能够实现测量和动目标指示等功能, 给干扰引导带来一定的困难。对此，提出随机频率模板的方法, 对雷达程控捷变频行为进行建模, 并设计了一种双胞循环神经网络识别程控捷变频行为。仿真实验结果表明, 双胞循环神经网络能够有效识别雷达程控捷变频行为, 并以一定的概率预测未来的频率序列, 能够有效地为窄带瞄准式干扰提供引导。仿真结果也表明, 所提网络能够有效记忆和识别一组非线性时间序列。

关键词: 捷变频, 行为识别, 循环神经网络, 记忆细胞

Abstract:

Radar program-controlled frequency agility behavior has a certain ability to resist narrow-band aiming jamming, and can realize the functions of measurement and moving target indication, which brings some difficulties to jamming guidance. For this, a random frequency template method is proposed to model the program-controlled frequency agility behavior of radar, and a bi-cell recurrent neural network (BRNN) is designed to identify the program-controlled frequency agility behavior. The simulation results show that the BRNN can effectively identify the frequency agility behavior of radar program-controlled, and predict the future frequency sequence with a certain probability, which can effectively provide guidance for narrow-band aiming jamming. The simulation results also show that the proposed network can effectively remember and identify a group of nonlinear time sequence.

Key words: frequency agility, behavior recognition, recurrent neural network (RNN), memory cell

中图分类号:

TN972

孟宪鹏, 刘利民, 董健, 王力, 胡文华. 基于双胞循环神经网络的雷达捷变频行为识别[J]. 系统工程与电子技术, 2024, 46(3): 898-905.

Xianpeng MENG, Limin LIU, Jian DONG, Li WANG, Wenhua HU. Radar frequency agility behavior recognition based on bi-cell recurrent neural network[J]. Systems Engineering and Electronics, 2024, 46(3): 898-905.

图/表 9

图1

图2

图3

图4

表1

表2

表3

图5

图6

参考文献 41

1	石荣, 肖悦. 行为科学的新分支: 电磁辐射源行为学[J]. 航天电子对抗, 2018, 34 (4): 1- 6. doi: 10.3969/j.issn.1673-2421.2018.04.001
	SHI R , XIAO Y . A new branch of behavioral science: electromagnetic radiation source behaviorology[J]. Aerospace Electronic Warfare, 2018, 34 (4): 1- 6. doi: 10.3969/j.issn.1673-2421.2018.04.001
2	WRABEL A , GRAEF R , BROSCH T . A survey of artificial intelligence approaches for target surveillance with radar sensors[J]. IEEE Aerospace and Electronic Systems Magazine, 2021, 36 (7): 26- 43. doi: 10.1109/MAES.2021.3065069
3	ALTMANN M, OTT P, STACHE N C, et al. A cognitive FMCW radar to minimize a sequence of range-Doppler measurements[C]//Proc. of the 17th European Radar Conference, 2021: 226-229.
4	GURBUZ S Z , GRIFFITHS H D , CHARLISH A , et al. An overview of cognitive radar: past, present, and future[J]. IEEE Aerospace and Electronic Systems Magazine, 2019, 34 (12): 6- 18. doi: 10.1109/MAES.2019.2953762
5	SKOLNIK M . Radar handbook[M]. 3rd ed New York: McGraw-Hill Companies, 2008.
6	GUO S Z , TRACEY H . Discriminant analysis for radar signal classification[J]. IEEE Trans.on Aerospace and Electronic Systems, 2020, 56 (4): 3134- 3148. doi: 10.1109/TAES.2020.2965787
7	LEVANON N , MOZESON E . Radar signals[M]. Hoboken, NJ: John Wiley & Sons, 2004.
8	NEUBERGER N , VEHMAS R . A costas-based waveform for local range-Doppler sidelobe level reduction[J]. IEEE Signal Processing Letters, 2021, 28, 673- 677. doi: 10.1109/LSP.2021.3067219
9	CORRELL B , BEARD J K , SWANSON C N . Costas array waveforms for closely spaced target detection[J]. IEEE Trans.on Aerospace and Electronic Systems, 2020, 56 (2): 1045- 1076. doi: 10.1109/TAES.2019.2925486
10	DEEP R E A , KASTANTIN R . Costas-code-based radar waveform design using adaptive weights with target scattering coefficients and optimal variable time spacing with improved ambiguity function[J]. IET Radar, Sonar & Navigation, 2020, 14 (12): 1905- 1917.
11	MENG X P , SHANG C X , DONG J , et al. Automatic modulation classification of noise-like radar intrapulse signals using cascade classifier[J]. ETRI Journal, 2021, 43 (6): 991- 1003. doi: 10.4218/etrij.2020-0338
12	LI D J , YANG R J , DONG R J , et al. Emitter signals modulation recognition based on discriminative projection and collaborative representation[J]. IET Radar, Sonar & Navigation, 2020, 14 (5): 782- 791.
13	CHI K , SHEN J H , LI Y , et al. A novel segmentation approach for work mode boundary detection in MFR pulse sequence[J]. Digital Signal Processing, 2022, 126, 103462. doi: 10.1016/j.dsp.2022.103462
14	LI Y J , ZHU M T , MA Y H , et al. Work modes recognition and boundary identification of MFR pulse sequences with a hie-rarchical seq2seq LSTM[J]. IET Radar, Sonar & Navigation, 2020, 14 (9): 1343- 1353.
15	APFELD S, CHARLISH A, ASCHEID G. Modelling, learning and prediction of complex radar emitter behaviour[C]//Proc. of the 18th IEEE International Conference on Machine Learning and Applications, 2019: 305-310.
16	KRISHNAMURTHY V , PATTANAYAK K , GOGINENI S , et al. Adversarial radar inference: inverse tracking, identifying cognition, and designing smart interference[J]. IEEE Trans.on Aerospace and Electronic Systems, 2020, 57 (4): 2067- 2081.
17	APFELD S , CHARLISH A . Recognition of unknown radar emitters with machine learning[J]. IEEE Trans.on Aerospace and Electronic Systems, 2021, 57 (6): 4433- 4447. doi: 10.1109/TAES.2021.3098125
18	MANNA M L, MONSURRÒ P, TOMMASINO P, et al. Machine learning techniques for frequency sharing in a cognitive radar[C]//Proc. of the IEEE Radar Conference, 2018: 732-735.
19	LI K, JIU B, LIU H W, et al. Reinforcement learning based anti-jamming frequency hopping strategies design for cognitive radar[C]//Proc. of the IEEE International Conference on Signal Processing, Communications and Computing, 2018.
20	茅于海. 频率捷变雷达分析[M]. 北京: 国防工业出版社, 1981.
	MAO Y H . Frequency agility radar[M]. Beijing: National Defense Industry Press, 1981.
21	李红卫, 蔡金元, 王玉田, 等. 机载有源相控阵雷达的作战优势、性能对比及军事应用[J]. 国防科技, 2015, 36 (6): 69- 73.
	LI H W , CAI J Y , WANG Y T , et al. The operational advantage, performance comparison and military application of airborne APAR[J]. National Defense Science & Technology, 2015, 36 (6): 69- 73.
22	HUANG T Y , LIU Y M , XU X Y , et al. Analysis of frequency agile radar via compressed sensing[J]. IEEE Trans.on Signal Processing, 2018, 66 (23): 6228- 6240. doi: 10.1109/TSP.2018.2876301
23	AKHTAR J, OLSEN K E. Frequency agility radar with overlapping pulses and sparse reconstruction[C]//Proc. of the IEEE Radar Conference, 2018: 61-66.
24	WANG W S, YANG Y L, LI Y Y. Multi-step prediction of frequency hopping sequences based on Bayesian inference[C]//Proc. of the IET International Conference on Information and Communications Technologies, 2013: 94-99.
25	ZHAO L , WANG L , BI G A , et al. Robust frequency-hopping spectrum estimation based on sparse Bayesian method[J]. IEEE Trans.on Wireless Communications, 2015, 14 (2): 781- 793. doi: 10.1109/TWC.2014.2360191
26	LIU X Q , SIDIROPOULOS N D , SWAMI A . Joint hop timing and frequency estimation for collision resolution in FH networks[J]. IEEE Trans.on Wireless Communications, 2005, 4 (6): 3063- 3074. doi: 10.1109/TWC.2005.858006
27	FAN H N, GUO Y, FENG X. Blind parameter estimation of frequency hopping signals based on matching pursuit[C]//Proc. of the 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008.
28	SAPANKEVYCH N I , SANKAR R . Time series prediction using support vector machines: a survey[J]. IEEE Computational Intelligence Magazine, 2009, 4 (2): 24- 38. doi: 10.1109/MCI.2009.932254
29	LEI Z W, ZHENG L H, DING H, et al. Prediction and separation of synchronous-networking frequency hopping signals based on RBF neural network[C]//Proc. of the 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, 2016: 427-431.
30	李文情. 跳频信号的检测[D]. 西安: 西安电子科技大学, 2012.
	LI W Q. Frequency-hopping signal's detection[D]. Xi'an: Xidian University, 2012.
31	LI G, XU J L, SHEN W G, et al. LSTM-based frequency hopping sequence prediction[C]//Proc. of the International Conference on Wireless Communications and Signal Processing, 2020: 472-477.
32	张家树. 混沌信号的非线性自适应预测技术及其应用研究[D]. 成都: 电子科技大学, 2001.
	ZHANG J S. Nonlinear adaptive prediction technologies of chaotic signals and its applications[D]. Chengdu: University of Electronic Science and Technology of China, 2001.
33	全英汇, 方文, 沙明辉, 等. 频率捷变雷达波形对抗技术现状与展望[J]. 系统工程与电子技术, 2021, 43 (11): 3126- 3136.
	QUAN Y H , FANG W , SHA M H , et al. Present situation and prospects of frequency agility radar waveform countermea-sures[J]. Systems Engineering and Electronics, 2021, 43 (11): 3126- 3136.
34	GRAVES A . Supervised sequence labelling with recurrent neural networks[M]. Berlin: Springer-Verlag, 2012.
35	HE H, ZHANG Q, BAI S M, et al. CATN: cross attentive tree-aware network for multivariate time series forecasting[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2022: 4030-4038.
36	SOOD A, CRAVEN M. Feature importance explanations for temporal Black-Box models[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2022: 8351-8360.
37	WANG H, AHN E, KIM J M. Self-supervised representation learning framework for remote physiological measurement using spatiotemporal augmentation loss[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2022: 2431-2439.
38	LIN H T, GAO Z Y, XU Y J, et al. Conditional local convolution for spatio-temporal meteorological forecasting[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2022: 7470-7478.
39	GOODFELLOW I , BENGIO Y , COURVILLE A . Deep learning[M]. Cambridge: MIT Press, 2017.
40	YU L, CHEN J, DING G R. Spectrum prediction via long short term memory[C]//Proc. of the 3rd IEEE International Conference on Computer and Communications, 2017: 643-647.
41	YU L , CHEN J , DING G R , et al. Spectrum prediction based on Taguchi method in deep learning with long short-term me-mory[J]. IEEE Access, 2018, 6, 45923- 45933. doi: 10.1109/ACCESS.2018.2864222

正确率	N_step
正确率	16	14	12	10	8
p₁^t	79.13	78.88	79.49	79.92	79.34
p₈^t	54.18	53.87	54.92	55.16	54.93

K	BP	RBF	Volterra	LSTM	BRNN
p₁^t	5.23	8.38	9.01	80.83	79.34

正确率	N_train
正确率	10 512	8 409	6 307	4 204	2 102
p₁^t	79.34	79.32	78.21	75.81	66.88
p₈^t	54.93	54.58	53.07	50.61	41.79

[1]	杨德贵, 许道峰. 基于时频域特征融合的IR-UWB穿墙雷达人体行为识别方法[J]. 系统工程与电子技术, 2024, 46(3): 849-858.
[2]	张亮, 杜庆磊, 周必雷, 瞿奇哲, 王永良. 基于非标准Keystone变换的捷变频雷达相参积累算法[J]. 系统工程与电子技术, 2023, 45(12): 3836-3844.
[3]	何诗扬, 汪玲, 朱岱寅, 钱君. 基于时空记忆解耦RNN的雷暴预测方法[J]. 系统工程与电子技术, 2023, 45(11): 3474-3480.
[4]	杜思予, 全英汇, 沙明辉, 方文, 邢孟道. 基于进化PSO算法的稀疏捷变频雷达波形优化[J]. 系统工程与电子技术, 2022, 44(3): 834-840.
[5]	丁逊, 张劲东, 王娜, 王玉莹. 基于相参积累的捷变频雷达系统相位误差估计与稀疏场景重构算法[J]. 系统工程与电子技术, 2021, 43(6): 1515-1523.
[6]	吴子龙, 陈红, 雷迎科. 改进DenseNet在电台建链行为识别时的可视化研究[J]. 系统工程与电子技术, 2021, 43(5): 1371-1381.
[7]	高诗飏, 董会旭, 田润澜, 张歆东. 基于SRNN+Attention+CNN的雷达辐射源信号识别方法[J]. 系统工程与电子技术, 2021, 43(12): 3502-3509.
[8]	张宏鹏, 黄长强, 轩永波, 唐上钦. 用门控循环单元实时预测空战飞行轨迹[J]. 系统工程与电子技术, 2020, 42(11): 2546-2552.