Satellite navigation is critical in signal-degraded environments where signals are corrupted and GNSS systems do not guarantee an\naccurate and continuous positioning. In particular measurements in urban scenario are strongly affected by gross errors, degrading\nnavigation solution; hence a quality check on the measurements, defined as RAIM, is important. Classical RAIM techniques work\nproperly in case of single outlier but have to be modified to take into account the simultaneous presence of multiple outliers.\nThis work is focused on the implementation of random sample consensus (RANSAC) algorithm, developed for computer vision\ntasks, in the GNSS context. This method is capable of detecting multiple satellite failures; it calculates position solutions based on\nsubsets of four satellites and compares them with the pseudoranges of all the satellites not contributing to the solution. In this\nwork, amodification to the original RANSA Cmethod is proposed and an analysis of its performance is conducted, processing data\ncollected in a static test.
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