At the ION GNSS+ 2018 conference, I presented a paper based on a joint effort between NRCan and Swift Navigation on network modelling considerations for wide-area ionospheric corrections. Both organizations had similar questions regarding this topic: what is the recommended station density to reach cm-level ionospheric corrections? And is it better to use a 3D model or a per-satellite model? This blog post summarizes our findings.
Obtaining mm-level positioning accuracies with GNSS requires modeling of all error sources such as higher-order ionospheric effects. As a part of an IAG working group, I collaborated with European colleagues to investigate how this error source could be estimated as a part of the PPP filter. The results were published last week in GPS Solutions (Banville et al. 2017).
For almost two years now, I have been extracting precise slant ionospheric delays from over 200 permanent GNSS stations in Canada using PPP-AR. Since there is no official data format for storing GNSS-derived slant ionospheric delays, I have developed by trial and error a format that is at least suitable for my own purposes. Since precise ionospheric delays are key to fast PPP convergence, we can certainly foresee a need for exchanging this kind of information in the future.
It is well known that the 'leveling' process, in which carrier phases are fitting to code observations, is a source of error in the determination slant total electron content (STEC). These leveling errors can be greatly attenuated when using carrier-phase ambiguities obtained from PPP as leveling information. In this post, I show how leveling errors can also impact VTEC and, potentially, DCB estimates.