Enhancing GNSS Positioning Accuracy in Challenging Environments: Development and Validation of a Multipath Prediction Tool

Despite the increasing number of satellites in multi-constellation GNSS, signal availability and quality remain significant problems in urban and forested environments. Field obstacles such as buildings and dense vegetation can lead to severe multipath issues. Various methods have been developed to mitigate multipath effects on measurement results, including optimizing antenna placement, selecting appropriate antenna and receiver types, and employing advanced post-processing techniques. However, these efforts have been unable to completely eliminate multipath interference, which can greatly affect positioning accuracy. The authors of this presentation have developed a tool that helps identify and remove reflected signals from measurement data sets. This tool, called GNSS MPD, was developed to predict satellite signal obstructions. It considers Line of Sight (LOS) vectors between specific locations and satellite positions and obstacle models derived from airborne LiDAR data. The LiDAR data is automatically acquired from geoportal.gov.pl, enabling the generation of an approximate terrain cover model. Satellite obstructions are validated using a ray-casting method. As part of testing the developed platform, the authors designed two experiments. The first experiment is a comparative analysis between satellite visibility scenarios obtained from GNSS MPD calculations and hemispherical photography. The second study involves performing positioning using information regarding the satellite visibility from GNSS MPD software. As part of this study, five 24-hour measurement sessions were conducted in a highly urbanized area.  Based on the receiver's approximate position, satellite visibility scenarios are generated using the developed platform. Static positioning measurements were performed in the experiment, yielding two sets of results: one based on raw receiver observations and the other incorporating visibility scenarios from the platform to adjust the observation files. The test results demonstrate improvements in both accuracy and the success rate of position determination.