ACSE Networks and Autocorrelation Features for PRI Modulation Recognition

Abstract

Pulse repetition interval (PRI) modulation recognition plays an important role in electronic warfare. Conventional recognition methods based on handcrafted features and elaborate threshold values suffer from the accuracy for multiple PRI modulations at low signal-to-noise ratio (SNR) with high percentages of missing pulses. In this letter, a method based on Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks and features in autocorrelation domain is proposed to recognize six PRI modulation modes automatically. First, features in the time domain, frequency domain and autocorrelation domain are converted to images. Then the images are input into ACSE networks which can extract and learn deep features without complex data pre-processing. Finally, a linear layer will output modulation modes directly. Via simulations, robustness of autocorrelation features is proved. The simulation results also demonstrate that the proposed recognition method can achieve higher than 91% accuracy at -12 dB under normal conditions for six modulations and higher than 95% at -4 dB under extreme conditions. Compared with the conventional SVM and three CNN methods, ACSE networks outperform at low SNRs under extreme conditions.