Hypothesis Testing to Adapt Measurement Noise Covariance for Smartphone GNSS Positioning

Anurag Raghuvanshi, Sunil Bisnath

Abstract:	Smartphone-based Global Navigation Satellite System (GNSS) positioning has advanced considerably with the introduction of dual-frequency chips and raw measurement APIs. However, raw measurements from smartphone GNSS receivers remain noisy and biased, with limited antenna performance and strong susceptibility to multipath and non-line-of-sight (NLOS) effects. These issues degrade the performance of tightly coupled GNSS/IMU solutions that rely on precise modeling of measurement noise covariance. This paper introduces a hypothesis testing-based adaptive Kalman filter, where standardized innovations are subjected to a t-test to adaptively scale measurement noise covariance based on carrier-to-noise (C/N0)-based weighting. The statistical approach avoids arbitrary thresholds and accounts for dynamic variations in smartphone measurements. The method is evaluated using datasets from the Google Smartphone Decimeter Challenge (2021–2023) across multiple devices (Xiaomi Mi8, Pixel 5, Pixel 7 Pro). Results show that the proposed approach reduces horizontal positioning error by 35–45% at the 50th and 95th percentiles compared to weighted least squares, consistently suppressing extreme outliers. The findings highlight the importance of statistically grounded adaptiveness for robust smartphone GNSS positioning and demonstrate progress toward decimeter-level accuracy. Future work will investigate process noise adaptation and machine learning-based covariance modeling.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	1144 - 1153
Cite this article:	Raghuvanshi, Anurag, Bisnath, Sunil, "Hypothesis Testing to Adapt Measurement Noise Covariance for Smartphone GNSS Positioning," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 1144-1153. https://doi.org/10.33012/2025.20318
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