Testing of a Statistical Approach for Local Ionospheric Disturbances Detection

Global Navigation Satellite Systems (GNSSs) may become a viable guidance means for safety-critical applications, such as the final approach and landing phases of a flight. To this purpose, Ground-Based Augmentation Systems (GBAS) are designed to enhance the navigation service, in terms of both accuracy and integrity. One of the tasks performed by GBAS stations is the timely and reliable detection of atmospheric disturbances that may compromise safety through biased or erroneous solutions. We address in this work the problem of detecting bi-dimensional ionospheric disturbances via GNSS carrier phase measurements from small-scale networks. Carrier phase measurements enable higher sensitivity for bias detection, but their inherent ambiguous nature has to be properly addressed. T he reliable detection of biases is performed through standard Detection, Identification and Adaptation (DIA) techniques, and the impact of both observation noise and local baseline geometry (lengths and mutual orientations) is analyzed.