Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network

Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen&#...

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Vydané v:	IEEE access Ročník 7; s. 112339 - 112347
Hlavní autori:	Qu, Zhiyu, Wang, Wenyang, Hou, Changbo, Hou, Chenfan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Classification Codes Cohen class time frequency distribution convolutional denoising autoencoder deep convolutional neural network Electronic warfare Feature extraction Frequency distribution Interpolation Modulation Neural networks Noise Noise reduction Pulse modulation Radar imaging Radar signal recognition Recognition Signal to noise ratio Time-frequency analysis
ISSN:	2169-3536, 2169-3536
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Abstract	Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen's time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is −9 dB.
AbstractList	Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen's time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is -9 dB. Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen's time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is −9 dB.
Author	Qu, Zhiyu Wang, Wenyang Hou, Changbo Hou, Chenfan
Author_xml	– sequence: 1 givenname: Zhiyu surname: Qu fullname: Qu, Zhiyu organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China – sequence: 2 givenname: Wenyang orcidid: 0000-0003-4744-0268 surname: Wang fullname: Wang, Wenyang organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China – sequence: 3 givenname: Changbo surname: Hou fullname: Hou, Changbo email: houchangbo@hrbeu.edu.cn organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China – sequence: 4 givenname: Chenfan surname: Hou fullname: Hou, Chenfan organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
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SubjectTerms	Artificial neural networks Classification Codes Cohen class time frequency distribution convolutional denoising autoencoder deep convolutional neural network Electronic warfare Feature extraction Frequency distribution Interpolation Modulation Neural networks Noise Noise reduction Pulse modulation Radar imaging Radar signal recognition Recognition Signal to noise ratio Time-frequency analysis
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Title	Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network
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