Radar emitter identification based on attention mechanism and improved CLDNN

被引：0
|
作者：
Cui B. [1 ]
Tian R. [1 ]
Wang D. [2 ]
Cui G. [1 ]
Shi J. [3 ]
机构：
[1] School of Aviation Operations and Services, Aviation University of Air Force, Changchun
[2] Air Force Research Institute, Beijing
[3] School of Aeronautical Foundation, Aviation University of Air Force, Changchun
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 05期
关键词：
Attention mechanism; Deep learning; Emitter identification; Feature fusion; One-dimensional convolutional long-short-term-memory deep neural networks(1CLDNN); Time series;
D O I：
10.12305/j.issn.1001-506X.2021.05.09
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1224 / 1231
页数：7
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