Map-Aided GNSS Satellite Selection for Urban Canyon Positioning
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Solution Overview
Problem
GNSS receivers face challenges in accurately determining location under weak signal conditions, such as in urban canyons, due to obstructed line-of-sight to satellites and multipath effects, leading to significant position errors and increased computational requirements.
Innovation Solution
A method and device that utilize a future trajectory estimation and environment modeling to select a subset of navigation satellites that will remain visible along the predicted path, incorporating map data, camera imagery, and inertial measurements to enhance satellite selection criteria, thereby reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS receiver uses signals from four or more satellites to determine accurate three-dimensional location, then positioning accuracy is improved, but computational requirements and processing complexity increase
Solution Approach 1:
The system performs preliminary satellite selection by predicting future satellite visibility along the estimated trajectory before actual positioning calculations are needed. This advance preparation identifies which satellites will be visible in upcoming time windows, allowing the receiver to pre-configure its tracking and measurement processes for only those relevant satellites, thereby reducing computational load while maintaining accuracy requirements
2Reliability
If GNSS receiver tracks more satellites to maintain positioning accuracy under weak signal conditions, then positioning reliability is improved, but power consumption increases
Solution Approach 1:
The system estimates the device's future trajectory using inertial sensors and map data, then predicts which satellites will be visible along this path. By knowing in advance which satellites will be visible during upcoming time windows, the receiver can power down tracking for satellites that will not be visible, while maintaining continuous tracking only for predicted visible satellites, thus reducing power consumption while ensuring positioning reliability when needed
Solution Approach 2:
The satellite tracking configuration is made dynamic and time-varying based on predicted trajectory and visibility. The system continuously updates which satellites to track based on the estimated future position and environmental model, adjusting the active satellite set over time to match actual visibility conditions, thereby optimizing power consumption while maintaining reliability
3Loss of information
If GNSS receiver processes signals from all visible satellites, then measurement data completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary filtering of the satellite set by predicting visibility along the estimated trajectory and environmental constraints. This pre-processing step identifies the subset of satellites that will be visible during upcoming time windows, allowing the receiver to focus computational resources only on these relevant satellites for measurement and positioning calculations, reducing processing time while maintaining data completeness for visible satellites
Solution Approach 2:
The system extracts and separates the relevant subset of satellites from the complete visible satellite set based on predicted visibility along the trajectory. By isolating only those satellites that will be visible during upcoming time windows, the system removes unnecessary satellites from processing, thereby reducing computational burden and processing time while retaining all necessary measurement data from actually visible satellites
Data Source
AI summary
Techniques for map-aided satellite selection are provided. An example of a method for determining a location according to the disclosure includes determining a future trajectory of a user equipment, estimating an environment model associated with the future trajectory, determining a plurality of expected navigation satellites based on the future trajectory and the environment model, selecting a set of navigation satellites to use in computing the current location based in part on the plurality of expected navigation satellites, and computing the current location based on the selected set of navigation satellites.