Radio frequency (RF) fingerprinting is a challenging and important technique in individual identification of wireless devices. Recent work has used deep learning-based classifiers on ADS-B signal without missing aircraft ID information. However, traditional methods are difficult to obtain well performance accuracy for classical deep learning methods to recognize RF signals. This letter proposes a Gaussian Low-pass Channel Attention Convolution Network (GLCA-Net), where a Gaussian Low-pass Channel Attention module (GLCAM) is designed to extract fingerprint features with low frequency. Particularly, in GLCAM, we design a Frequency-Convolutional Global Average Pooling (F-ConvGAP) module to help channel attention mechanism learn channel weights in frequency domain. Experimental results on the datasets of large-scale real-world ADS-B signals show that our method can achieve an accuracy of 92.08%, which is 6.21% higher than Convolutional Neural Networks.