ELoran signal message prediction algorithm based on Alexnet‐ECA

Abstract

Abstract Enhanced Loran (eLoran) is the most important backup system for Global Navigation Satellite System (GNSS). However, most current eLoran demodulation methods are based on digital signal processing, which may produce large errors under the influence of strong interference or noise. The authors propose an eLoran signal message prediction algorithm based on deep learning. Using time‐frequency (TF) analysis convert one‐dimensional signal into two‐dimensional high‐resolution image. It improves Alexnet by adjusting the convolutional layer structure, adding an attention module, and reducing the pooling layer window to construct the Alexnet‐ECA network. The network is used to identify TF image features and predict signal message. The experimental results show that novel method for eLoran signal message prediction based on Alexnet‐ECA can achieve high accuracy and robustness in various SNR scenarios. The accuracy is more than 90% at SNR greater than 10db and not less than 80% even in the complex environments. Compared with Resnet34 and VGG16 networks, Alexnet‐ECA demonstrates its efficiency and effectiveness using real measurement data. This method can enhance the performance and reliability of eLoran receivers and applications.

Keywords

• Loran
• neural networks
• signal processing
• time‐frequency analysis