基于注意力机制和改进CLDNN的雷达辐射源识别

doi:10.12305/j.issn.1001-506X.2021.05.09

Abstract

Abstract:

Traditional emitter identification is based on the comparison and matching of emitter signal and radar database, which is difficult to meet the requirements of high efficiency, fast and accurate identification in wartime. With the development of machine learning methods, such as the application of support vector machine (SVM) and other algorithm in the field of emitter identification, can meet the requirements of efficient and rapid identification in wartime. However, this method has low accuracy of emitter identification in low signal to noise ratio environment. In order to solve the above problems, the deep learning is used, the attention mechanism and feature fusion method is introduced, and a indentification model of attention-mechanism feature-fusion one-dimensional convolution long-short-term-memory deep neural networks (AF1CLDNN) is proposed. The effectiveness of attention mechanism and feature fusion method is verified by experiments, and the new indentification model has high indentification accuracy and indentification speed in low signal to noise ratio environment.

Key words: emitter identification, deep learning, time series, attention mechanism, feature fusion, one-dimensional convolutional long-short-term-memory deep neural networks(1CLDNN)

CLC Number:

TN971.1

Bangyan CUI, Runlan TIAN, Dongfeng WANG, Gang CUI, Jingyuan SHI. Radar emitter identification based on attention mechanism and improved CLDNN[J]. Systems Engineering and Electronics, 2021, 43(5): 1224-1231.

Figures/Tables 12

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References 34

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信号类型	载频/ kHz	采样频率/ kHz	主要参数
BPSK	1~1.2	7	7, 11, 13位巴克码
Costas	1~1.2	{15, 17}	—
Frank	1~1.2	7	36, 64位码
P1~P4	1~1.2	7	36, 64位码

序号	网络	有无特征融合	训练轮数	训练时间	损失	准确率/%
1	F1CLDNN	有	15	0:16:27	0.130 1	96.02
2	1CLDNN	无	62	5:30:36	0.366 5	86.47

序号	网络	有无注意力	训练轮数	训练时间	损失	准确率/%
1	F1CLDNN	有	10	0:03:46	0.118 2	96.47
2	F1CLDNN	无	15	0:16:27	0.130 1	96.02
3	1CLDNN	有	10	0:55:12	0.291 1	90.99
4	1CLDNN	无	62	5:30:36	0.366 5	86.47

序号	网络	训练时间	训练轮数	损失	准确率/%
1	AF1CLDNN	0:03:46	10	0.118 2	96.47
2	F1CLDNN	0:16:27	15	0.130 1	96.02
3	A1CLDNN	0:55:12	10	0.291 1	90.99
4	1CLDNN	5:30:36	62	0.366 5	86.47
5	DNN	0:05:08	50	0.119 4	96.00
6	Resnet34	0:07:08	7	0.193 3	94.80
7	GoogleNet	0:14:44	13	0.343 7	88.02
8	AlexNet	0:05:55	5	0.261 1	93.39

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Radar emitter identification based on attention mechanism and improved CLDNN

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