|Abstract:||A map-aiding algorithm is added to the estimation of the full geometry-based float solution for differential carrier phase positioning of a land vehicle in order to assess the effect of map matching on the convergence of the position and ambiguity states. As this constraint leads to highly non-Gaussian posterior densities, it is implemented within the framework of Bayesian theory, using a Sequential Importance Resampling (SIR) Particle Filter (PF). This PF estimates the user position, velocity, and acceleration states, as well as the float ambiguities using L1 GPS carrier phase and pseudorange observations. The position accuracy of the Particle Filter solution with and without the map-aiding constraint is compared to the typically used Extended Kalman Filter (EKF). The proposed algorithm is tested in four different segments of a larger land vehicle data set, showing how the position convergence improves when adding digital road map information within the first thirty seconds of initializing the PF in different scenarios that include driving in a straight line, turning, and changing lanes.|
Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
|Pages:||2941 - 2953|
|Cite this article:||
Manzano-Islas, Rene, O’Keefe, Kyle, "SIR Particle Filter in Float Solution with Map-Aiding Algorithm," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 2941-2953.
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