SIR Particle Filter in Float Solution for Ambiguity Resolution

Rene Manzano-Islas and Kyle O’Keefe

Abstract: In this paper, we implement a Sequential Importance Resampling (SIR) Particle Filter (PF) for estimating the full geometrybased float solution state vector for Global Navigation Satellite System (GNSS) ambiguity resolution. This PF estimates the user position, velocity and acceleration states, as well as the float ambiguities using L1 GPS carrier phase and pseudorange observations. We estimate an empirical covariance matrix Pk from the distribution of the particles after resampling based on the incorporated measurements of each epoch. This will allow the particle distribution to be transformed using the integer decorrelating Z transformation of the LAMBDA method. We assess the performance of a float solution based on point mass representation compared to the typically used Extended Kalman Filter (EKF) for searching the integer ambiguities using the three common search methods described in [1], Integer Rounding, Integer Bootstrapping and Integer Least Squares with and without an application of the Z transformation.
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: 2493 - 2506
Cite this article: Manzano-Islas, Rene, O’Keefe, Kyle, "SIR Particle Filter in Float Solution for Ambiguity Resolution," 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. 2493-2506.
https://doi.org/10.33012/2021.17949
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