Abstract. The densification of GNSS Continuously Operating Reference Station (CORS) networks in mountainous regions is constrained by the high cost of geodetic-grade equipment. Low-cost (LC) multi-frequency GNSS receivers offer a viable alternative, yet their performance in challenging high-altitude Alpine environments remains largely unexplored. This study evaluates the rover-side positioning performance and tropospheric delay estimation capability of a newly installed LC permanent station at Prali (2200 m elevation), in the Alpine region of Piedmont, Italy. The station, based on a u-blox ZED-F9P receiver with a broadband LC antenna and a Raspberry Pi computer, was assessed using Virtual Reference Station (VRS) corrections from the SPIN3 professional CORS network. Six independent two-hour RTK sessions across a full diurnal cycle were processed using RTKLIB in forward-only kinematic mode to emulate real-time conditions. Results demonstrate that the LC station achieves centimetre-level horizontal precision (8–11 mm) with fix rates up to 97% and time to first fix below 3 minutes under favourable conditions. A diurnal performance variability was observed and characterised across the six sessions. Zenith Tropospheric Delay estimation via CSRS-PPP with 92% fixed ambiguities yielded physically consistent values (mean ZTD = 1811 mm, ZWD = 41 mm), consistent with dry winter conditions at altitude. These results confirm that LC GNSS stations can deliver reliable centimetre-level positioning and meaningful tropospheric products in demanding Alpine environments, supporting their deployment for CORS network densification in regions where geodetic-grade infrastructure is economically or logistically prohibitive.
M. Bagheri, P. Dabove, A. Masiero et al.· The International Archives o...· 0 citations
Abstract. Carrier phase observations enable millimeter-level GNSS positioning, but their continuity is frequently disrupted by signal blockages and cycle slips. This limitation is particularly critical for low-cost and smartphone receivers, where weak antennas, urban multipath, and duty cycling cause frequent phase gaps that prevent reliable ambiguity resolution. Before addressing the full complexity of mass-market observations, the prediction methodology must be validated under controlled conditions. In this work we investigate whether machine learning, supported by precise satellite orbits and clocks, can predict carrier phase observations during signal gaps with millimeter-level accuracy. Twenty-four hours of Galileo data from the TORI permanent station (SPIN3 network, Torino, Italy) are processed at 30 s sampling using GFZ final SP3 and CLK products. After forming the ionosphere-free combination, an iterative carrier-phase based estimator removes the receiver clock, tropospheric delay, and ambiguity, reducing the residuals to a median standard deviation of 60 mm. Synthetic gaps from 60 s to 1800 s are introduced (1045 gaps total) and four prediction strategies are compared: polynomial fitting (degrees 3 and 5), Fourier-augmented polynomial, Gradient Boosting Regression with satellite geometry features, and Gaussian Process Regression. The Gradient Boosting model achieves the best overall performance, reaching 4.4 mm RMS for 60 s gaps, 9.4 mm for 5 min gaps, and 21 mm for 30 min gaps, well below the half-wavelength threshold required for cycle slip repair. These results demonstrate that geometry-aware gap prediction is feasible at the sub-wavelength level, providing a validated foundation for extending the approach to low-cost and smartphone GNSS receivers.
P. Dabove, M. Bagheri, N. Gogoi· The International Archives o...· 0 citations
