Abstract
Satellite navigation is critical in signal-degraded environments where signals are corrupted and GNSS systems do not guarantee an accurate and continuous positioning. In particular measurements in urban scenario are strongly affected by gross errors, degrading navigation solution; hence a quality check on the measurements, defined as RAIM, is important. Classical RAIM techniques work properly in case of single outlier but have to be modified to take into account the simultaneous presence of multiple outliers. This work is focused on the implementation of random sample consensus (RANSAC) algorithm, developed for computer vision tasks, in the GNSS context. This method is capable of detecting multiple satellite failures; it calculates position solutions based on subsets of four satellites and compares them with the pseudoranges of all the satellites not contributing to the solution. In this work, a modification to the original RANSAC method is proposed and an analysis of its performance is conducted, processing data collected in a static test.
Highlights
GNSS are worldwide and all weather navigation systems are able to provide threedimensional position, velocity, and time synchronization to UTC scale
The main disadvantage of GNSS is the need of having a good satellites visibility; for this reason, the environments characterized by strong signal degradation, such as urban areas, are critical for satellite navigation [2]
The results obtained from P-random sample consensus (RANSAC) algorithm are compared with original RANSAC method and the ones obtained with a classical RAIM technique, that is, the “observation subset testing” [13, 14]
Summary
GNSS (global navigation satellite systems) are worldwide and all weather navigation systems are able to provide threedimensional position, velocity, and time synchronization to UTC (coordinated universal time) scale. Buildings can reflect signals from in-view satellites, producing the multipath effects, or from non-line-of-sight (NLOS) ones. These two effects are not the same, they sometimes occur together, but cause gross errors in measurements even though different [3]. It is no longer possible to assume that the probability of failure for more than one satellite within a certain timeframe is negligible In this context the ability to detect multiple satellite failures at the receiver level, where local errors sources as multipath, NLOS reception, receiver failures, unusual atmospheric conditions, or interference degrade the navigation solution, becomes of high importance for the integrity of the navigation system [4]. The results obtained from P-RANSAC algorithm are compared with original RANSAC method and the ones obtained with a classical RAIM technique, that is, the “observation subset testing” [13, 14]
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