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Ionospheric phase scintillation can cause errors or outage in GNSS navigation solutions. Timely detection of phase scintillation will enable adaptive processing to mitigate its effects on navigation solutions. This paper presents a machine-learning algorithm to autonomously detect phase scintillation based on frequency domain features. Validation using data from Gakona shows phase scintillation detection accuracy around 92 percent. Test results using data from Poker Flat, Jicamarca, Singapore, and Hong Kong demonstrate the capability of the trained detector to be applied more generally. Performance evaluation reveals that the values of phase scintillation index σ ϕ may be poor indications of scintillation activities. Concurrent phase and amplitude scintillation detection using similar machine-learning algorithms is further investigated with low-latitude data. Results suggest that at low latitudes an amplitude detector alone is sufficient to capture scintillation in general, while at high latitudes, a phase scintillation detector is necessary to capture the dominating phase scintillation events.

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