Intrapulse Modulation Radar Signal Recognition Using CNN with Second-Order STFT-Based Synchrosqueezing Transform

Abstract

Intrapulse modulation classification of radar signals plays an important role in modern electronic reconnaissance, countermeasures, etc. In this paper, to improve the recognition rate at low signal-to-noise ratio (SNR), we propose a recognition method using the second-order short-time Fourier transform (STFT)-based synchrosqueezing transform (FSST2) combined with a modified convolution neural network, which we name MeNet. In particular, the radar signals are first preprocessed via the time–frequency analysis and STFT-based FSST2. Then, the informative features of the time–frequency images (TFIs) are deeply learned and classified through the MeNet with several specific convolutional blocks. The simulation results show that the overall recognition rate for seven types of intrapulse modulation radar signals can reach 95.6%, even when the SNR is −12 dB. Compared with other networks, the excellent recognition rate proves the superiority of our method.

Keywords

• intrapulse radar signal modulation classification
• convolutional neural network (CNN)
• time–frequency image
• short-time Fourier transform (STFT)
• second-order STFT-based synchrosqueezing transform (FSST2)