Radar emitter signal recognition based on MSST and HOG feature extraction

Aiming at the problem of low recognition accuracy of traditional radar signal recognition algorithms under low signal-to-noise ratio, a radar emitter recognition algorithm based on multi-synchrosqueezing transform (MSST) time-frequency transformation and histogram of direction gradient (HOG) feature extraction is proposed. The algorithm performs multiple synchronous compression processing on the basis of the short-time Fourier transform (STFT) of the radar time domain signal to obtain the signal time-frequency distribution image, then uses the HOG operator to extract the HOG feature of the signal time-frequency distribution image. The HOG features are dimensionally reduced by principal component analysis (PCA), and finally the feature parameters after dimension reduction are fed into the support vector machine (SVM) to classify and identify the radar signal. The experimental results show that the algorithm has low complexity, and when the signal-to-noise ratio is −8 dB, the recognition accuracy of the simulation experiments and hardware-in-the-loop simulation experiments for 9 typical radar signals can reach more than 90%. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.