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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

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 languageEnglish
Title of host publicationProceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021
PublisherInstitute of Navigation
Pages3094-3102
Number of pages9
ISBN (Electronic)9780936406299
DOIs
StatePublished - 2021
Event34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021 - St. Louis, United States
Duration: 20 Sep 202124 Sep 2021

Publication series

NameProceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021

Conference

Conference34th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2021
Country/TerritoryUnited States
CitySt. Louis
Period20/09/2124/09/21

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.

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