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

Abstract

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

CLC Number:

TN973

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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References 15

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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

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

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