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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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 112339 - 112347
Main Authors: Qu, Zhiyu, Wang, Wenyang, Hou, Changbo, Hou, Chenfan
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2935247