Radar signal recognition based on triplet convolutional neural network

Abstract Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to ident...

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Autores principales:	Lutao Liu, Xinyu Li
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	LPI radar signal recognition Triplet loss Convolutional neural network Deep learning Smooth pseudo-Wigner–Ville distribution Telecommunication TK5101-6720 Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/08fe32933c9840a08fb85473ce86cd80
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spelling	oai:doaj.org-article:08fe32933c9840a08fb85473ce86cd802021-11-14T12:15:47ZRadar signal recognition based on triplet convolutional neural network10.1186/s13634-021-00821-81687-6180https://doaj.org/article/08fe32933c9840a08fb85473ce86cd802021-11-01T00:00:00Zhttps://doi.org/10.1186/s13634-021-00821-8https://doaj.org/toc/1687-6180Abstract Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.Lutao LiuXinyu LiSpringerOpenarticleLPI radar signal recognitionTriplet lossConvolutional neural networkDeep learningSmooth pseudo-Wigner–Ville distributionTelecommunicationTK5101-6720ElectronicsTK7800-8360ENEURASIP Journal on Advances in Signal Processing, Vol 2021, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	LPI radar signal recognition Triplet loss Convolutional neural network Deep learning Smooth pseudo-Wigner–Ville distribution Telecommunication TK5101-6720 Electronics TK7800-8360
spellingShingle	LPI radar signal recognition Triplet loss Convolutional neural network Deep learning Smooth pseudo-Wigner–Ville distribution Telecommunication TK5101-6720 Electronics TK7800-8360 Lutao Liu Xinyu Li Radar signal recognition based on triplet convolutional neural network
description	Abstract Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.
format	article
author	Lutao Liu Xinyu Li
author_facet	Lutao Liu Xinyu Li
author_sort	Lutao Liu
title	Radar signal recognition based on triplet convolutional neural network
title_short	Radar signal recognition based on triplet convolutional neural network
title_full	Radar signal recognition based on triplet convolutional neural network
title_fullStr	Radar signal recognition based on triplet convolutional neural network
title_full_unstemmed	Radar signal recognition based on triplet convolutional neural network
title_sort	radar signal recognition based on triplet convolutional neural network
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/08fe32933c9840a08fb85473ce86cd80
work_keys_str_mv	AT lutaoliu radarsignalrecognitionbasedontripletconvolutionalneuralnetwork AT xinyuli radarsignalrecognitionbasedontripletconvolutionalneuralnetwork
_version_	1718429329037197312

Radar signal recognition based on triplet convolutional neural network

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