Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network

Abstract

In this paper, to solve the problem of the low recognition rate of the existing approaches at low signal-to-noise ratio (SNR), an intra-pulse modulation recognition approach for radar signal is proposed. The approach identifies the modulation of radar signals using the techniques of time-frequency analysis, image processing, and convolutional neural network (CNN). Through Cohen class time-frequency distribution (CTFD), the time-frequency images (TFIs) of received signals are extracted. In order to obtain the high-quality TFIs of received signals, we introduce a new kernel function for the CTFD, which has stronger anti-noise ability than Choi-Williams time-frequency distribution. A series of image processing techniques, including 2-D Wiener filtering, bilinear interpolation, and Otsu method, are applied to remove the background noise of the TFI and obtain a fixed-size binary image that contains only morphological features of the TFI. We design a CNN classifier to identify the processed TFIs. The proposed approach can identify up to 12 kinds of modulation signals, including frequency modulation, phase modulation, and composite modulation. Simulation results show that, for 12 kinds of modulation signals, the proposed approach achieves an overall probability of successful recognition of 96.1% when SNR is -6 dB.

Keywords

• Radar signal recognition
• Cohen class time frequency distribution
• convolutional neural network