GNSS Spoofing Detection through Metric Combinations: Calibration and Application of a General Framework

Fabian Rothmaier, Leila Taleghani, Yu-Hsuan Chen, Sherman Lo, Eric Phelts, Todd Walter

Abstract: This paper is an application example of a general framework to combine an arbitrary number of metrics for GNSS spoofing detection using the Generalized Likelihood Ratio Test (GLRT). The framework maximizes robustness to wide ranges of attack modes while guaranteeing a false alert probability under the Neyman-Pearson paradigm. In this paper we summarize the previously published GLRT framework and analyze its performance against conventional means to combine metrics through logical gates. For a combination of power and signal distortion metrics the worst case PMD is reduced by 60%. We then calibrate measurement models for a receiver model based on flight data and validate compliance with the false alert guarantee for power and a signal distortion metric as well as pseudorange residuals. We apply the framework using the calibrated models to spoofing scenarios from the TEXBAT dataset processed through the same receiver type. We demonstrate highly robust detection behavior by leveraging the framework to combine the three metrics.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 4249 - 4263
Cite this article: Rothmaier, Fabian, Taleghani, Leila, Chen, Yu-Hsuan, Lo, Sherman, Phelts, Eric, Walter, Todd, "GNSS Spoofing Detection through Metric Combinations: Calibration and Application of a General Framework," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 4249-4263.
https://doi.org/10.33012/2021.18126
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