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.
Original language | English |
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Title of host publication | Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021 |
Publisher | Institute of Navigation |
Pages | 3094-3102 |
Number of pages | 9 |
ISBN (Electronic) | 9780936406299 |
DOIs | |
State | Published - 2021 |
Event | 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021 - St. Louis, United States Duration: 20 Sep 2021 → 24 Sep 2021 |
Publication series
Name | Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021 |
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Conference
Conference | 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021 |
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Country/Territory | United States |
City | St. Louis |
Period | 20/09/21 → 24/09/21 |
Bibliographical note
Publisher Copyright:© 2021 Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021. All rights reserved.