Title: How Reliable is a Virtual RINEX?
Author(s): Paolo Dabove, Alberto Cina, Ambrogio Maria Manzino
Published in: Proceedings of IEEE/ION PLANS 2016
April 11 - 14, 2016
Hyatt Regency Hotel
Savannah, GA
Pages: 255 - 262
Cite this article: Dabove, Paolo, Cina, Alberto, Manzino, Ambrogio Maria, "How Reliable is a Virtual RINEX?," Proceedings of IEEE/ION PLANS 2016, Savannah, GA, April 2016, pp. 255-262.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In
Abstract: Virtual RINEX (VR) is a common product for Global Navigation Satellite System (GNSS) post-processing applications, useful if the distance between the rover and master is greater than 30 km. Many studies have been conducted in order to analyse the content of this kind of data, but few have proved the positioning performances obtainable in particular conditions. The VR is a synthetic file created from real data by software that manages continuous operating reference system networks. It depends on algorithms of the software to compute and model the bias interpolation. This paper focuses attention on the evaluation of the quality and accuracy of positioning using VR near the border of two different continuous operating reference system networks, managed by two different network software (GNSMART provided by GEO++ and Spidernet by Leica Geosystems) in the same reference system. To assess the reliability and quality of VR provided by two different GNSS network software with inter-station distances of about 40 km (typical of Italian networks), 15 points equally distributed near the border between the two networks were chosen, using points with the same distances between real existing stations of both networks. For each point, a VR file 24 hours in length was generated with a sampling rate of 1 second. Tests were performed to assess the importance of the location of permanent stations at different altitudes to estimate biases for the VR generation in a better way. To assess the quality of the VR of the two networks, the baseline vector between the VR generated by the two network software was evaluated using relative positioning. The files have been processed for session lengths of 5 minutes, 10 minutes, 30 minutes, 1 hour, and 24 hours.