An innovative method to predict and to detect the false fixing of the GNSS ambiguity phase

Abstract

One of the most critical points during the GNSS NRTK (Network Real Time Kinematic) positioning is the correct fixing of the ambiguity phase. 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 ambiguity phase.