Environment adaptive navigation (EAN): A data-driven approach for improved navigation accuracy

Abstract

The quality of GNSS-based navigation services is highly influenced by the type of operating environment. The urban environments with buildings and structures pose substantial challenges for GNSS navigation accuracy.  To address this challenge, we propose a data-driven approach Environment Adaptive Navigation (EAN) because complex mathematical models for GNSS environments are impractical for real-time use due to their processing load. This data-driven approach analyzes real-time GNSS data to understand how the environment affects performance and optimize receiver settings for each scenario. Keeping this in view, raw GNSS data was collected through field trials including the three environments: clear sky, partially degraded, and highly degraded. We then analyzed this data to pinpoint factors affecting accuracy, such as the number of available satellites and standard error measurements. The proposed solution, the EAN algorithm, tackles these limitations of the GNSS due to the urban environments and improves the navigation performance. This data-driven approach analyzes real-time GNSS data to identify the specific environment (clear, partially degraded, or highly degraded). Based on this assessment, EAN dynamically adjusts receiver settings, like tracking loop bandwidth, to achieve optimal performance under those conditions. Integrating the EAN model into GNSS receivers allows for real-time environment detection and adaptive configuration. This EAN-based GNSS receiver holds significant promise for safety-critical applications like Intelligent Transportation Systems (ITS). Precise navigation is crucial for functionalities within ITS, such as route optimization and autonomous vehicle operation. The effectiveness of the EAN-based receiver was validated through a field experiment demonstrating a notable increase in tracked satellites and a substantial reduction in outages within a highly degraded environment.