一种深度神经网络多站雷达系统干扰鉴别方法

doi:10.19665/j.issn1001-2400.2021.02.017

西安电子科技大学学报 ›› 2021, Vol. 48 ›› Issue (2): 133-138.doi: 10.19665/j.issn1001-2400.2021.02.017

一种深度神经网络多站雷达系统干扰鉴别方法

刘洁怡¹(),公茂果¹(),詹涛²(),李豪¹(),张明阳¹()

1.西安电子科技大学电子工程学院,陕西西安 710071
2.西安电子科技大学计算机科学与技术学院,陕西西安 710071

收稿日期:2020-07-06 修回日期:2020-12-14 出版日期:2021-04-20 发布日期:2021-04-28
作者简介:刘洁怡(1991—),女,讲师,博士,E-mail: liujieyi0220@163.com|公茂果(1979—),男,教授,博士,E-mail: mggong@xidian.edu.cn|詹涛(1990—),男,讲师,博士,E-mail: tzhan@xidian.edu.cn|李豪(1990—),男,副教授,博士,E-mail: haoli@xidian.edu.cn|张明阳(1988—),男,讲师,博士,E-mail: myzhang@xidian.edu.cn
基金资助:
国家自然科学基金(61906146);国家自然科学基金(61906147);陕西省自然科学基金(2020JQ-313);陕西省自然科学基金(2019JQ-417);中央高校基本科研业务费专项资金(JB210211);2019年新教师创新基金(XJS190205)

Method for discrimination of false targets in multistation radar systems based on the deep neural network

LIU Jieyi¹(),GONG Maoguo¹(),ZHAN Tao²(),LI Hao¹(),ZHANG Mingyang¹()

1. School of Electronic Engineering,Xidian University,Xi’an 710071,China
2. School of Computer Science and Technology,Xidian University,Xi’an 710071,China

Received:2020-07-06 Revised:2020-12-14 Online:2021-04-20 Published:2021-04-28

摘要/Abstract

摘要：

针对现有多站雷达系统欺骗式干扰鉴别方法,仅利用目标回波空间相关性单一特征作为度量标准,导致特征提取全面性不够,鉴别算法有效性和普适性不足的问题,提出一种基于深度神经网络的多站雷达系统干扰鉴别方法,将多站雷达协同探测技术在空间、时间和频率域内可用资源多、调度能力强的特点,与深度神经网络很强的模型学习和特征表示能力相结合,有效地应用于欺骗式干扰对抗领域。充分利用回波数据的未知信息,获取除相关性之外更多维、更全面、更完善、更深层的特征差异,达到更优的干扰鉴别效果。仿真实验结果证明,提出的深度神经网络干扰鉴别方法可以有效地降低噪声和脉冲数量对干扰鉴别性能的影响,缓解非理想条件下目标回波相关系数对干扰对抗技术的限制,拓宽了应用过程的边界条件。

关键词: 多站雷达系统, 深度神经网络, 干扰鉴别, 特征提取

Abstract:

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.

Key words: multistation radar systems, deep neural network, jamming discrimination, feature extraction

中图分类号:

TN973

刘洁怡,公茂果,詹涛,李豪,张明阳. 一种深度神经网络多站雷达系统干扰鉴别方法[J]. 西安电子科技大学学报, 2021, 48(2): 133-138.

LIU Jieyi,GONG Maoguo,ZHAN Tao,LI Hao,ZHANG Mingyang. Method for discrimination of false targets in multistation radar systems based on the deep neural network[J]. Journal of Xidian University, 2021, 48(2): 133-138.

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参考文献 15

[1]	LUO M F, HAO Z M, ZHU Y M. Intelligent ECCM Technology via Cognition and Agility for the Airborne Radar[J]. Journal of Engineering, 2019,2019(20):6656-6659.
[2]	ZHANG J D, ZHU D Y, ZHANG G. New Antivelocity Deception Jamming Technique Using Pulses with Adaptive Initial Phases[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013,49(2):1290-1300.
[3]	LIU J, ZHANG L, ZHAO S, et al. Echo Correlation Analysis in Diversity Multiple-input Multiple-output Radars[J]. IET Radar,Sonar and Navigation, 2016,10(6):1112-1117. doi: 10.1049/rsn2.v10.6
[4]	LIU J, ZHANG L, ZHAO S, et al. Correlation Characteristic Analysis in Diversity Multiple-input Multiple-output Radar[J]. Electronics Letters, 2017,53(5):349-351. doi: 10.1049/ell2.v53.5
[5]	ZHAO S, LIU Z. Deception Parameter Estimation and Discrimination in Distributed Multiple-radar Archirectures[J]. IEEE Sensors Journal, 2017,17(19):6322-6330. doi: 10.1109/JSEN.2017.2737654
[6]	于恒力, 张林让, 刘洁怡, 等. 一种异构组网雷达系统抗欺骗式假目标方法[J]. 电子科技大学学报, 2019,48(5):706-710.
	YU Hengli, ZHANG Linrang, LIU Jieyi, et al. A Method against Deception-false-target Jamming Based on Isomerous Netted Radars[J]. Journal of University of Electronic Science and Technology of China, 2019,48(5):706-710.
[7]	于恒力, 张林让, 刘洁怡, 等. 一种对抗欺骗式干扰的组网雷达功率分配方法[J]. 西安交通大学学报, 2019,53(12):139-145.
	YU Hengli, ZHANG Linrang, LIU Jieyi, et al. A Power Allocation Approach for Anti-deception Jamming in Netted Radars[J]. Journal of Xi’an Jiaotong University, 2019,53(12):139-145.
[8]	刘洁怡, 张林让, 赵珊珊, 等. 欺骗式干扰下组网雷达优化布站方法分析与仿真[J]. 电子科技大学学报, 2017,46(4):513-519.
	LIU Jieyi, ZHANG Linrang, ZHAO Shanshan, et al. Analysis and Simulation of Optimal Deployment for Netted Radar Under Deception Jamming[J]. Journal of University of Electronic Science and Technology of China, 2017,46(4):513-519.
[9]	刘洁怡, 张林让, 赵珊珊, 等. 一种考虑站址误差的抗欺骗式假目标方法[J]. 西安交通大学学报, 2017,51(6):54-58.
	LIU Jieyi, ZHANG Linrang, ZHAO Shanshan, et al. A Method Against Deception-false-target Jamming Based on Position Error[J]. Journal of Xi’an Jiaotong University, 2017,51(6):54-58.
[10]	赵珊珊, 张林让, 周宇, 等. 利用空间散射特性差异进行有源假目标鉴别[J]. 西安电子科技大学学报, 2015,42(2):20-27. doi: 10.3969/j.issn.1001-2400.2015.02.004
	ZHAO Shanshan, ZHANG Linrang, ZHOU Yu, et al. Active False-target Discrimination Method Based on the Difference in Spatial Scattering Characteristic[J]. Journal of Xidian University, 2015,42(2):20-27. doi: 10.3969/j.issn.1001-2400.2015.02.004
[11]	KEPHART O. Learning from Nature[J]. Science, 2011,331(6018):682-683. doi: 10.1126/science.1202093 pmid: 21310989
[12]	HINTON G E, SALAKHUTDINOV R R. Reducing the Dimensionality of Data with Neural Networks[J]. Science, 2006,313(5786):504-507. doi: 10.1126/science.1127647 pmid: 16873662
[13]	周志文, 黄高明, 高俊, 等. 一种深度学习的雷达辐射源识别算法[J]. 西安电子科技大学学报, 2017,44(3):77-82
	ZHOU Zhiwen, HUANG Gaoming, GAO Jun, et al. Radar Emitter Identification Algorithm Based on Deep Learning[J]. Journal of Xidian University, 2017,44(3):77-82.
[14]	SUN J, XU G, REN W, et al. Radar Emitter Classification Based on Unidimensional Convolutional Neural Network[J]. IET Radar,Sonar and Navigation, 2018,12(8):862-867. doi: 10.1049/rsn2.v12.8
[15]	ZHAO S S, ZHANG L R, ZHOU Y, et al. Discrimination of Active False Targets in Multistatic Radar Using Spatial Scattering Properties[J]. IET Radar,Sonar and Navigation, 2016,10(5):817-826. doi: 10.1049/rsn2.v10.5

隐含网络层数	目标噪声比/dB					网络结构
隐含网络层数	0	3	6	9	12	网络结构
1	90.51	96.43	98.89	99.32	99.69	[K×Q-100-2]
2	92.91	97.45	99.32	99.75	99.90	[K×Q-250-50-2]
3	94.37	98.33	99.38	99.87	99.95	[K×Q-250-100-50-2]
4	94.89	98.69	99.54	99.75	99.95	[K×Q-250-100-50-10-2]
5	94.41	98.70	99.50	99.86	99.95	[K×Q-250-100-50-10-5-2]
6	94.58	98.47	99.61	99.71	99.91	[K×Q-250-180-100-50-10-5-2]

鉴别概率		欺骗式干扰回波相关系数
鉴别概率		1.0	0.9	0.8	0.7	0.6
	0.0	98.23	96.52	92.95	89.16	83.06
	0.1	97.74	94.95	92.21	87.21	78.05
真实目标相关系数	0.2	97.32	94.55	91.04	85.20	76.97
	0.3	95.33	93.13	88.27	81.22	74.18
	0.4	95.24	91.36	84.99	76.66	70.97

一种深度神经网络多站雷达系统干扰鉴别方法

Method for discrimination of false targets in multistation radar systems based on the deep neural network

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