Title: On-line Model Learning for Adaptive GNSS Ionospheric Scintillation Estimation and Mitigation
Author(s): Jordi Vilà-Valls, Carles Fernández-Prades, Javier Arribas, James T. Curran, Pau Closas
Published in: Proceedings of IEEE/ION PLANS 2018
April 23 - 26, 2018
Hyatt Regency Hotel
Monterey, CA
Pages: 1167 - 1172
Cite this article: Vilà-Valls, Jordi, Fernández-Prades, Carles, Arribas, Javier, Curran, James T., Closas, Pau, "On-line Model Learning for Adaptive GNSS Ionospheric Scintillation Estimation and Mitigation," Proceedings of IEEE/ION PLANS 2018, Monterey, CA, April 2018, pp. 1167-1172.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In
Abstract: One of the more challenging propagation conditions in Global Navigation Satellite Systems (GNSS), clearly affecting high-precision receivers and safety critical applications, is ionospheric scintillation. One of the main disadvantages of standard phase tracking architectures is the estimation vs mitigation tradeoff, that is, it is impossible to distinguish between the line-of-sight (LOS) phase contribution of interest and phase variations induced by the ionosphere. A possible solution is to consider a state-space formulation of the problem, where the scintillation amplitude and phase are estimated together with the LOS phase variations. The current Kalman filter-based techniques exploiting this idea consider an off-line scintillation model fitting, thus not suitable for real-life applications. In this contribution we propose an adaptive model learning strategy to be able to cope with time-varying unknown scintillation conditions. The performance of the new approach is shown using real ionospheric scintillation data recorded over Hanoi in 2015.