Precise Positioning with Machine Learning based Kalman Filter using GNSS/IMU Measurements from Android Smartphone

Kahee Han, Subin Lee, Young-Jin Song, Hak-Beom Lee, Dong-Hyuk Park, Jong-Hoon Won

Abstract:	This paper presents GNSS/INS integration Kalman filter for enhancement of positioning accuracy and robustness to surrounding environment. In the Kalman filter system, filter parameters such as process noise covariance and measurement noise covariance selected in the tuning process determine the characteristics of the overall system. Therefore, the empirical knowledge of the filter designer should be fully employed in the tuning process, and finding proper parameter values is still a challenging work. We adopt reinforcement learning to find the process noise covariance of the filter parameter. The experimental results show that the improvement of navigation performance is achieved by the efficient use of the learned process noise covariance matrix.
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:	3094 - 3102
Cite this article:	Han, Kahee, Lee, Subin, Song, Young-Jin, Lee, Hak-Beom, Park, Dong-Hyuk, Won, Jong-Hoon, "Precise Positioning with Machine Learning based Kalman Filter using GNSS/IMU Measurements from Android Smartphone," 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. 3094-3102. https://doi.org/10.33012/2021.18005
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