This paper presents a machine learning model able to distinguish between ionospheric scintillation and multipath in GNSS-based scintillation monitoring data. The inputs to the model are the average signal intensity, the variance in the signal intensity, and the covariance between the in-phase and the quadrature-phase outputs of the tracking loop of a GNSS receiver. The model labels the data as either scintillated, multipath affected, or clean GNSS signal. The overall accuracy of the model is 96% with 2% miss-detection rate and a negligible false alarm rate for the scintillation class in particular. The gain in the amount of scintillation data is up to 17.5% that would have been discarded if an elevation mask of 30° was implemented.

Distinguishing Ionospheric Scintillation from Multipath in GNSS Signals Using Bagged Decision Trees Algorithm / Imam, R.; Dovis, F.. - ELETTRONICO. - (2020), pp. 83-88. (Intervento presentato al convegno 8th Annual IEEE International Conference on Wireless for Space and Extreme Environments, WiSEE 2020 tenutosi a Vicenza, Italy nel 2020) [10.1109/WiSEE44079.2020.9262699].

Distinguishing Ionospheric Scintillation from Multipath in GNSS Signals Using Bagged Decision Trees Algorithm

Imam R.;Dovis F.
2020

Abstract

This paper presents a machine learning model able to distinguish between ionospheric scintillation and multipath in GNSS-based scintillation monitoring data. The inputs to the model are the average signal intensity, the variance in the signal intensity, and the covariance between the in-phase and the quadrature-phase outputs of the tracking loop of a GNSS receiver. The model labels the data as either scintillated, multipath affected, or clean GNSS signal. The overall accuracy of the model is 96% with 2% miss-detection rate and a negligible false alarm rate for the scintillation class in particular. The gain in the amount of scintillation data is up to 17.5% that would have been discarded if an elevation mask of 30° was implemented.
2020
978-1-7281-6451-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2927857