Magnetic Localization Through INS Integration and Improvements in Map Matching

Ryan McWilliams, Howard Chen, Luke Kamrath, David Bevly

Abstract:	Navigation by means of magnetic map-based particle filters has already been proven feasible in specific conditions and under strict adherance to mapped areas. Ambiguties inherent to the magnetic signal may be mitigated by fusing the filter with additional measurements, most accessibly from an accelerometer. Acceleration measurements can be used to improve the measurement update step by providing additional information for likelihood estimation. This approach was tested against a magnetometer-only filter and found improvements in the best-case and average performance, but greater variability in maximum error and decreased filter stability. Additionally, it was used to help gauge navigability and recoverability in instances of attempted localization not on the mapped route. Both approaches could recover from brief diversions from the map but could not overcome longer diversions that skipped segments of the map. Also introduced to help positioning estimation is spatial correlation analysis, a likelihood technique that takes into account multiple navigation samples and signal scaling. This technique is found to be competitive in accuracy but much more computationally demanding than the traditional aggregate bin likelihood technique.
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:	2272 - 2284
Cite this article:	McWilliams, Ryan, Chen, Howard, Kamrath, Luke, Bevly, David, "Magnetic Localization Through INS Integration and Improvements in Map Matching," 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. 2272-2284. https://doi.org/10.33012/2021.17906
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In