Can analytical techniques guarantee that GNSS errors are bounded under rare conditions? What techniques are available?

摘要

This article has introduced several analytical approaches to bounding unknown GNSS range-domain error distributions with Gaussian distributions, potentially augmented with non-Gaussian tail distributions. Analytical methods have three general advantages over the purely empirical approach of Part Ⅰ: 1.They provide additional flexibility in modeling low-probability (tail) error behavior, 2.They are more systematic and repeatable, and 3.They provide theoretical assurance that the resulting bounds hold at the required integrity probabilities under certain conditions. In practice, these advantages are useful but are less important than they might seem. The use of non-Gaussian models of errors in the tail leads to significantly tighter bounds, but it conflicts with the many existing applications whose protection level equations assume Gaussian-distributed tails only. Systematized algorithms help reduce variations due to individual judgement, but extensive judgment is still required, particularly when deciding how to collect representative error data, how to sort it into sets that represent different environmental conditions, and how much error data is needed to provide sufficient sampling of errors at low probabilities.