Multipath Mitigation via Clustering for Position Estimation Refinement in Urban Environments

Julian Gutierrez, Russell Gilabert, Evan Dill, Guillermo Hernandez, David Kaeli, and Pau Closas

Peer Reviewed

Abstract: Position estimation using global navigation satellite systems (GNSS) suffers from poor accuracy within urban canyons due to significant signal disruption caused by tall buildings. This issue can be attributed to the GNSS signals reflecting off buildings resulting in severe multipath reflections which degrade the receiver’s performance. In this paper, we introduce an innovative approach to filter GNSS satellite measurements to improve the accuracy of the estimated position by leveraging a clustering algorithm. This approach utilizes a predictive GNSS availability service to filter out non-line-of-sight measurements. Then, a subset of line-of-sight satellite measurement combinations are evaluated using a clustering algorithm. When combined, results show these techniques can reduce the mean horizontal error measured in an urban canyon by nearly an order of magnitude, from ? 18 meters to ? 2 meters when using a single point positioning solver.
Published in: Proceedings of the ION 2024 Pacific PNT Meeting
April 15 - 18, 2024
Hilton Waikiki Beach
Honolulu, Hawaii
Pages: 556 - 568
Cite this article: Gutierrez, Julian, Gilabert, Russell, Dill, Evan, Hernandez, Guillermo, Kaeli, David, Closas, Pau, "Multipath Mitigation via Clustering for Position Estimation Refinement in Urban Environments," Proceedings of the ION 2024 Pacific PNT Meeting, Honolulu, Hawaii, April 2024, pp. 556-568. https://doi.org/10.33012/2024.19605
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