Model Parameter Optimization for GNSS Point Positioning in a Kinematic Ocean Buoy Application

Maria Gonzalez, Jason Gross

Abstract:	For kinematic GNSS Point Positioning, often, a trial and error approach is used for selecting model parameters of the state estimator, such as state process noise, measurement process noise, and initial uncertainty. This approach typically relies on user experience and background insight on the dynamics of the particular application. To alleviate this, we present a processing strategy to assess solution quality and to automatically optimize these filter model parameters. To evaluate the proposed approach, we consider the use of GNSS data collected from a kinematic ocean buoy. It was found that performing parameter optimization with a Genetic Algorithm was a suitable approach for achieving improved estimation performance.
Published in:	Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023) September 11 - 15, 2023 Hyatt Regency Denver Denver, Colorado
Pages:	53 - 66
Cite this article:	Gonzalez, Maria, Gross, Jason, "Model Parameter Optimization for GNSS Point Positioning in a Kinematic Ocean Buoy Application," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 53-66. https://doi.org/10.33012/2023.19182
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