Displacement monitoring using multi-technique antenna calibrations in processing GNSS data from multi-frequency low-cost receivers

Abstract

<p>In the framework of the EU founded GIMS-Project (Geodetic Integrated Monitoring System), a low-cost solution for detecting and measuring ground movements have been developed (https://www.gims-project.eu/). In particular, our focus was the improvement of processing techniques of multi-constellation GNSS data from low-cost devices, culminated in the development of improved algorithms implemented now in the goGPS open-source software (https://gogps-project.github.io). The goGPS engine has been used to analyse the motion of two different landslides in Slovenia. It is currently successfully employed to monitor, in near real-time, numerous landslides and structures.</p><p>During the last year, we developed an advanced technique to improve the on-site calibration of the low-cost antennas that allows a drastic improvement of the stability of sub-daily coordinates. This approach is based on iterative steps designed to filter the multipath affecting phase measurements and the phase center variation of the antennas.</p><p>A first solution using more than 10 days of data is computed with the goal of retrieving phase residuals. Then these residuals are filtered&#160;using a combination of Zernike polynomials interpolation and multi-resolution gridding in order to obtain hemi-spherical maps able to correct the observations of longer periods. A final solution is finally obtained applying the stored corrections.</p><p>Eventually the procedure can be iterated to reach convergence and maps can be updated after environmental changes.</p><p>By using these maps in a multi-constellation environment, the monitoring of displacements is feasible with low-cost receivers (single and multi-frequencies) even in challenging conditions. The accuracy improvements are higher than 50% with respect to a scenario without maps.</p><p>In this work, the processing procedure of low-cost receivers is presented with examples of successful monitoring experiences<strong>.</strong></p>