GNSS Performance Evaluation for Deep Urban Environments using GNSS Foresight

Abstract

Global Navigation Satellite System (GNSS) is an integral part of global positioning. However, because GNSS performance is impacted by signal obscuration and the presence of multipath in deep urban environments, it is not accurate, reliable, and available enough to be a standalone system in all environments. With GNSS being the most globally available and the most used positioning technology, this creates two problems: 1) The GNSS user does not know when or where GNSS performance may be degraded. 2) The GNSS user has limited ability to mitigate the issues. No mitigation exists to improve the availability of GNSS itself. Spirent’s GNSS Foresight service aims to solve both issues. A cloud-based solution, GNSS Foresight provides users with a precise prediction or forecast of GNSS performance for specific locations and times so that the system can plan to avoid those areas, constrain the operational mode, or use appropriate mitigation strategies. In addition, GNSS Foresight provides the GNSS receiver with GNSS satellite and signal information and these can be employed to support the decision-making strategy and calculations in the GNSS receiver to improve its positioning solution performance, integrity, and reliability. Currently, the foresight prediction service supports global and regional GNSS constellations. The GNSS foresight data can be provided combining the information from multiple satellites or constellations, or as information on individual satellite signals; or both. GNSS Foresight data can be employed to optimize the operation of the receiver’s positioning and measurement algorithms in order to improve the overall performance of the GNSS receiver. GNSS Foresight can provide information on a specific set of dates and location, for a time in the past, which can then be used to support diagnostic or forensic activities. This information can then be used by receiver developers to minimize the impact of multipath-induced errors in the position solution; and to improve the recovery from lead and lag events normally seen during dense urban GNSS positioning. The data can also be used by receiver developers to optimize their signal quality monitoring algorithms, as it provides information on which measurements are contaminated. The results presented here will show the correlation between the GNSS Foresight output and the GNSS position solution computed by different GNSS receivers in a deep urban environment. They will compare the forecasted LoS satellites with the satellites that are being tracked and used in the position solution by different GNSS receivers. The results will go on to show that the forecasted areas of degraded GNSS performance in deep urban environments correlate with the actual poor navigation accuracy of a GNSS device. Ultimately, the paper will highlight the advantages of integrating the GNSS foresight information into different stages of the GNSS position computation process in order to improve the position solution performance.