Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

Abstract

The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently, deep learning-based methods, especially convolutional neural networks (CNN), have been used in classification of intra-pulse modulation of radar emitter signals. However, those two-dimensional CNN-based methods, which require dimensional transformation of the original sampled signals in the stage of data preprocessing, are resource-consuming and poorly feasible. In order to solve these problems, we proposed a one-dimensional selective kernel convolutional neural network (1-D SKCNN) to accurately classify the intra-pulse modulation of radar emitter signals. Compared with other previous methods described in the literature, the data preprocessing of the proposed method merely includes zero-padding, fast Fourier transformation (FFT) and amplitude normalization, which is much faster and easier to achieve. The experimental results indicate that the proposed method has the advantages of faster speed in data preprocessing and higher accuracy in intra-pulse modulation classification of radar emitter signals.