UK company Envisage Space Ltd., working with Cranfield University and supported by the European Space Agency (ESA) NAVISP program, has developed analytical software designed to improve the reliability of GNSS positioning in dense urban environments.
At a recent ESA-hosted event, Terry McLarney of Envisage Space and Cranfield University Professor Ivan Petrunin presented the results of the ‘RelyMap Connect’ project, which focused on the needs of micromobility services such as shared e-scooters and bikes. These applications depend on accurate and reliable location information to enforce geofencing rules, manage parking zones and monitor vehicle movements.
Readers will know cities present some of the most challenging conditions for satellite navigation, significantly impacting applications that need reliable location information. RelyMap Connect addressed this challenge using predictive GNSS analytics combined with live measurement data. The system builds on Envisage Space’s RelyMap platform, which uses detailed three-dimensional city models to predict satellite visibility and expected GNSS performance at street level.
Real-time estimation
The new software estimates the expected quality of GNSS positioning in specific locations and time periods. The predictions are then combined with live, multi-constellation GNSS measurements and telemetry from connected devices to assess the reliability of the positioning solution in real time.
Machine-learning techniques are used to fuse predicted signal behavior with GNSS measurements, allowing the system to identify situations where urban signal conditions are likely to degrade positioning performance.
A key output is a ‘confidence factor’ associated with the computed position. This metric provides an indication of how reliable the GNSS solution is under current environmental conditions and can be used to determine whether positioning is sufficiently reliable for operational tasks such as enforcing virtual parking bays or no-go zones.
The project team demonstrated geofence optimization in urban micromobility deployments in several UK cities. By identifying areas where GNSS performance is expected to degrade, operators were able to adjust geofence boundaries or operational rules to improve compliance and reduce false violations caused by positioning errors.
The software operates as a cloud-based analytical platform capable of evaluating GNSS positioning performance across large urban areas, providing a continuously updated view of positioning reliability in complex city environments.
McLarney said the results demonstrated how environmental modeling and data analytics can complement conventional GNSS processing for emerging urban mobility applications. RelyMap Connect partners now plan to engage with micromobility operators, regulators and city authorities to explore commercial deployment of the technology.
