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.
Highlights
Radar signal intra-pulse modulation recognition is one of the key technologies in electronic warfare, such as electronic support and electronic intelligence (ES/ELINT), electronic attack (EA), and radar warning receiver (RWR)
When we make the training set, 200 samples are randomly generated from each signal at the same signal-tonoise ratio (SNR) condition and the SNR will increase from −10 dB to 10 dB at a step of 1 dB
In this paper, a radar intra-pulse modulation recognition approach based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed
Summary
Radar signal intra-pulse modulation recognition is one of the key technologies in electronic warfare, such as electronic support and electronic intelligence (ES/ELINT), electronic attack (EA), and radar warning receiver (RWR). Radar intrapulse modulation classification information based on the intercepted signal will provide the presence of threat and radar information, such that necessary measures or counter measures against enemy radars can be taken by the ES/EA system. Techniques such as pulse compression reduce the power spectral density of radar signals, which require that the intra-pulse modulation recognition approach of radar signals has good performance at a lower signal-tonoise ratio (SNR). The accuracy rate of the traditional radar signal recognition mainly relies on the feature extraction algorithm, but the artificial feature extraction relies mostly on the experience of the researchers, the extracted features are targeted to specific types of radar signals, and new features need to be extracted when identifying other signals [3], [4]
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