Radar Signal Modulation Recognition Based on Sep-ResNet

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yongjiang Mao, Wenjuan Ren, Zhanpeng Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	radar modulation signal time–frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/155dc6e7d8394f20990801edae434677
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:155dc6e7d8394f20990801edae434677
record_format	dspace
spelling	oai:doaj.org-article:155dc6e7d8394f20990801edae4346772021-11-25T18:56:44ZRadar Signal Modulation Recognition Based on Sep-ResNet10.3390/s212274741424-8220https://doaj.org/article/155dc6e7d8394f20990801edae4346772021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7474https://doaj.org/toc/1424-8220With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.Yongjiang MaoWenjuan RenZhanpeng YangMDPI AGarticleradar modulation signaltime–frequency analysiscomplex Morlet waveletimage enhancementchannel-separable ResNetChemical technologyTP1-1185ENSensors, Vol 21, Iss 7474, p 7474 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radar modulation signal time–frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet Chemical technology TP1-1185
spellingShingle	radar modulation signal time–frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet Chemical technology TP1-1185 Yongjiang Mao Wenjuan Ren Zhanpeng Yang Radar Signal Modulation Recognition Based on Sep-ResNet
description	With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.
format	article
author	Yongjiang Mao Wenjuan Ren Zhanpeng Yang
author_facet	Yongjiang Mao Wenjuan Ren Zhanpeng Yang
author_sort	Yongjiang Mao
title	Radar Signal Modulation Recognition Based on Sep-ResNet
title_short	Radar Signal Modulation Recognition Based on Sep-ResNet
title_full	Radar Signal Modulation Recognition Based on Sep-ResNet
title_fullStr	Radar Signal Modulation Recognition Based on Sep-ResNet
title_full_unstemmed	Radar Signal Modulation Recognition Based on Sep-ResNet
title_sort	radar signal modulation recognition based on sep-resnet
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/155dc6e7d8394f20990801edae434677
work_keys_str_mv	AT yongjiangmao radarsignalmodulationrecognitionbasedonsepresnet AT wenjuanren radarsignalmodulationrecognitionbasedonsepresnet AT zhanpengyang radarsignalmodulationrecognitionbasedonsepresnet
_version_	1718410508118261760

Radar Signal Modulation Recognition Based on Sep-ResNet

Ejemplares similares