Un metodo innovativo per predire ed identificare i falsi fissaggi delle ambiguità di fase GNSS in reti di stazioni permanenti

One of the most critical points during the GNSS NRTK (Network Real Time Kinematic) positioning is the correct fixing of the phase ambiguity. This work wants to try to focus attention on the quality control of the real-time GNSS positioning, both from the point of view of what the network provides, and from one of the network products is used by the rover receiver. The quality of the positioning is a parameter that must be monitored in real time to avoid an incorrect ambiguity fixing, also called FF (false fixing), occurring; this can be due both to internal problems of the network software and, more often, to the environment (obstructions, multipath and so on) within which where the receiver works. To achieve this control a tool was designed that, starting from the data available in real time from a user connected to an NRTK positioning service, can identify with a certain probability threshold the effective presence, or the possibility, of a false fixing. The FF estimator will be composed of a neural network, trained a priori with some datasets, and will have, as a single output, the probability that the current fixing is a false fixing of the phase ambiguity. Interesting and surprising results with geodetic GNSS receivers were obtained: in fact FFs are not only identified but also predicted correctly in 95% of cases, regardless of differential correction and the size of the network of permanent stations. In this work only parameters available in real time from the user were considered, but in the future the goal will be to consider also some network parameters in order to analyze why there are still unexplained FFs.