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Open access Aug 2026

Improving GNSS Performances in Location-Based Services through Synthetic Carrier-Phase Measurements

This work investigates whether machine learning, supported by precise satellite orbits and clocks, can predict carrier phase observations during signal gaps with millimeter-level accuracy and demonstrates that geometry-aware gap prediction is feasible at the sub-wavelength level.

P. Dabove, M. Bagheri, N. Gogoi · 0 citations

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