Intra-pulse modulation radar signal recognition based on Squeeze-and-Excitation networks

In this paper, a novel recognition method based on the squeeze-and-excitation networks (SE-Nets) is proposed in order to recognize intra-pulse modulation signals at varying noise levels automatically. Firstly, different signal transforms including time domain, frequency domain and time–frequency domain are used to convert seven different intra-pulse modulation signals into images. Then, since the SE-Net has great advantages in image processing, the images are classified by the squeeze-and-excitation networks and output the results in each domain. Lastly, the decisions of different domains are combined to obtain the final results. The simulation results demonstrate that the recognition accuracies are all more than 95% except BPSK signals which are still more than 90% at the case of − 8 dB. Compared with several other neural networks and traditional support vector machine method, the accuracy of SE-Net method has increased more than 2% and performs better under different SNR conditions. The measured signals results show that the accuracies of the SE-Net method are higher than those of several other neural networks, especially for BASK and BFSK signals.