Recognition of Radar Active Jamming Signal Based on Attention Mechanism

Aiming at the problem that radar active jamming signal cannot be recognized effectively in complex background, a recognition algorithm of radar active jamming signal based on attention mechanism is proposed. Firstly, the time-frequency map of the interference signal is input into the feature extract...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Chen Tao, Li Jun, Wang Xiangyang, Huang Xiangsong
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Aero Weaponry 2021
Materias:	\|attention mechanism\|feature extraction\|recognition of jamming\|active jamming\|rpn\|radar signal Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/178c1070ddd54ca689c8e5befbcdb74f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:178c1070ddd54ca689c8e5befbcdb74f
record_format	dspace
spelling	oai:doaj.org-article:178c1070ddd54ca689c8e5befbcdb74f2021-11-30T00:13:23ZRecognition of Radar Active Jamming Signal Based on Attention Mechanism1673-504810.12132/ISSN.1673-5048.2020.0206https://doaj.org/article/178c1070ddd54ca689c8e5befbcdb74f2021-10-01T00:00:00Zhttps://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/1636698950263-1885713724.pdfhttps://doaj.org/toc/1673-5048Aiming at the problem that radar active jamming signal cannot be recognized effectively in complex background, a recognition algorithm of radar active jamming signal based on attention mechanism is proposed. Firstly, the time-frequency map of the interference signal is input into the feature extraction network to obtain the depth feature. Then, the attention mechanism is introduced to locate the location of interference signal through RPN network to get the region of interest. Finally, the type of interference signal is identified for the region of interest. The simulation results show that the algorithm can locate, recognize and separate jamming signals in complex background. Compared with convolution neural network, the training sample is reduced by half, and the training efficiency is improved. At the same time, the overall recognition accuracy is improved by 10%, which can reach 95%.Chen Tao, Li Jun, Wang Xiangyang, Huang XiangsongEditorial Office of Aero Weaponryarticle\|attention mechanism\|feature extraction\|recognition of jamming\|active jamming\|rpn\|radar signalMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 5, Pp 86-91 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|attention mechanism\|feature extraction\|recognition of jamming\|active jamming\|rpn\|radar signal Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|attention mechanism\|feature extraction\|recognition of jamming\|active jamming\|rpn\|radar signal Motor vehicles. Aeronautics. Astronautics TL1-4050 Chen Tao, Li Jun, Wang Xiangyang, Huang Xiangsong Recognition of Radar Active Jamming Signal Based on Attention Mechanism
description	Aiming at the problem that radar active jamming signal cannot be recognized effectively in complex background, a recognition algorithm of radar active jamming signal based on attention mechanism is proposed. Firstly, the time-frequency map of the interference signal is input into the feature extraction network to obtain the depth feature. Then, the attention mechanism is introduced to locate the location of interference signal through RPN network to get the region of interest. Finally, the type of interference signal is identified for the region of interest. The simulation results show that the algorithm can locate, recognize and separate jamming signals in complex background. Compared with convolution neural network, the training sample is reduced by half, and the training efficiency is improved. At the same time, the overall recognition accuracy is improved by 10%, which can reach 95%.
format	article
author	Chen Tao, Li Jun, Wang Xiangyang, Huang Xiangsong
author_facet	Chen Tao, Li Jun, Wang Xiangyang, Huang Xiangsong
author_sort	Chen Tao, Li Jun, Wang Xiangyang, Huang Xiangsong
title	Recognition of Radar Active Jamming Signal Based on Attention Mechanism
title_short	Recognition of Radar Active Jamming Signal Based on Attention Mechanism
title_full	Recognition of Radar Active Jamming Signal Based on Attention Mechanism
title_fullStr	Recognition of Radar Active Jamming Signal Based on Attention Mechanism
title_full_unstemmed	Recognition of Radar Active Jamming Signal Based on Attention Mechanism
title_sort	recognition of radar active jamming signal based on attention mechanism
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/178c1070ddd54ca689c8e5befbcdb74f
work_keys_str_mv	AT chentaolijunwangxiangyanghuangxiangsong recognitionofradaractivejammingsignalbasedonattentionmechanism
_version_	1718406846920785920

Recognition of Radar Active Jamming Signal Based on Attention Mechanism

Ejemplares similares