Gaussian low‐pass channel attention convolution network for RF fingerprinting

Abstract

Radio frequency (RF) fingerprinting is a challenging and important technique for individual identification of wireless devices. Recent work has applied deep learning‐based classifiers to ADS‐B signals without missing aircraft ID information. However, traditional methods are not very effective in achieving high accuracy for deep learning models to recognize RF signals. In this letter, a Gaussian low‐pass channel attention convolution network, which uses a Gaussian low‐pass channel attention module (GLCAM) to extract fingerprint features with low frequency. Specifically, in GLCAM, a frequency‐convolutional global average pooling module is designed to help the channel attention mechanism learn channel weights in the frequency domain. Experimental results on large‐scale real‐world ADS‐B signal datasets show that the method can achieve an accuracy of 92.08%, which is 6.21% higher than convolutional neural networks.