Performance Assessment of Fault Free Recursive ARAIM with High-Integrity Time-Correlated Measurement Error Models

Elisa Gallon, Samer Khanafseh, Boris Pervan, Mathieu Joerger

Abstract: In this paper, we develop a new Kalman-filter (KF)-based approach for Global Navigation Satellite Systems (GNSS) positioning, fault detection, and integrity monitoring. The filter design integrates stochastic measurement error models developed and validated in prior work using multiple years of data (Gallon et al., 2020, 2021, 2022). These models account for uncertain measurement error time-correlation using power spectral density (PSD) bounding (Langel et al., 2020). They are used in this paper to provide a realistic performance assessment of recursively-implemented Advanced Receiver Autonomous Integrity Monitoring (ARAIM).
Published in: Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
Denver, Colorado
Pages: 1455 - 1466
Cite this article: Gallon, Elisa, Khanafseh, Samer, Pervan, Boris, Joerger, Mathieu, "Performance Assessment of Fault Free Recursive ARAIM with High-Integrity Time-Correlated Measurement Error Models," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1455-1466. https://doi.org/10.33012/2022.18419
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