Algorithms for precise point positioning with ambiguity resolution (PPP-AR) were developed over a decade ago. Since then, techniques have matured and most analysis centres (ACs) of the International GNSS Service (IGS) now produce products enabling PPP-AR. The IGS PPP-AR working group, created during the 2018 IGS workshop in Wuhan, investigated the interoperability of such products and recently published a paper on this topic in the Journal of Geodesy.
To obtain the highest accuracy from precise point positioning (PPP) solutions, consistency between network and user algorithms must be ensured. An error source prone to mismodelling is satellite attitude, especially during eclipse periods. This blog post explains how to use data from an attitude exchange format being tested within the IGS.
Will PPP ever replace RTK? Over the past decade, we have seen the convergence of PPP and RTK towards “PPP-RTK,” where satellite orbit/clock/bias corrections are augmented by local atmospheric corrections to enable instantaneous convergence to cm-level accuracies. But how close are we to instantaneous cm-level PPP-AR, without local augmentation?
Precise point positioning (PPP) requires careful modeling of several error sources affecting GNSS observations. Additionally, consistency between the network and user software is essential to obtain the upmost accuracy. Unfortunately, an inconsistency affecting the CSRS-PPP software has been causing, for a few years now, a height bias of several millimeters.
The online PPP service offered by the Canadian Geodetic Survey of NRCan has been available since 2003. It processes, on average, about 1000 RINEX files daily, fulfilling the positioning needs of Canadians and the international community. The PPP landscape having evolved significantly in the last few years, the underlying PPP engine will be replaced by a new version on August 14th 2018.
When I first got involved in GNSS, more than a decade ago, my objective was to reduce the convergence time of PPP solutions. In the past few years, I witnessed this methodology evolve and fast convergence became possible using ambiguity resolution and external atmospheric data. The upcoming years will be a game changer in this area: with GNSS modernization, instantaneous PPP convergence will be possible even without any reference stations nearby.
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).
With ongoing work at NRCan aiming at offering an online PPP service supporting ambiguity resolution, we performed a validation exercise consisting of processing nearly 40 permanent GPS stations in eastern Canada over a 10-year period. As a by-product of this analysis, we computed station velocities and compared them with the values derived from the Bernese network solutions done at NRCan. The results were published last week in Survey Review and I am offering a short summary here.
Hardware delays, or biases, affect GNSS carrier-phase and code measurements and must be properly accounted for in high-accuracy positioning. Several models were proposed to handle biases in precise point positioning with ambiguity resolution (PPP-AR), all of which can be cast in an uncombined representation. In this post, I explain the unified processing scheme that I am using in my software to deal with common PPP-AR products.
The extension of network RTK to larger networks is facilitated by a state-space representation of error sources, and is often associated with the term PPP-RTK. By adding atmospheric corrections to satellite orbit and clock corrections, it is possible to obtain fast convergence and seamless transition from a network RTK to a PPP solution. While this concept has been introduced nearly 15 years ago, there are still very few providers of PPP-RTK services at a global scale. Is this about to change?