Abstract: | In this study, transfer learning is used to detect the presence of spoofed signals at the acquisition stage of GNSS signal processing. Transfer learning is a deep learning technique that leverages the pre-knowledge of a pre-trained model from different type of task and data. To do this, an open-source deep learning library, called fast.ai, is used, and the selected model is ResNet-18. It is fine-tuned and evaluated with cross-ambiguity function (CAF) images of clean and spoofed signals in static and dynamic platform from TEXBAT and OAKBAT data. CAF images were generated from the 2D cross-correlation matrix output of each satellite at acquisition using an open-source GNSS software receiver called FGI-GSRx. The model was able to get an average of 94.6% accuracy. |
Published in: |
Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024) September 16 - 20, 2024 Hilton Baltimore Inner Harbor Baltimore, Maryland |
Pages: | 3656 - 3664 |
Cite this article: | Fredeluces, Ellarizza, Kubo, Nobuaki, "Classification of Spoofed and Non-Spoofed Cross-Ambiguity Function Image by Deep Learning Approach," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 3656-3664. https://doi.org/10.33012/2024.19904 |
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