GNSS Positioning with Learned Elevation Masks in Urban Obstruction
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Solution Overview
Problem
Conventional satellite-based location systems face challenges in accurately determining the position of devices in urban environments due to multipath reflections and signal attenuation caused by obstructions, which compromise the reliability and accuracy of location estimation, especially for applications like autonomous vehicle navigation.
Innovation Solution
A learned-elevation mask is generated using sensor data to filter out satellites without a direct line-of-sight, combining static elevation masks with learned masks based on environmental data from cameras and digital elevation maps to enhance location accuracy by discarding indirect satellite signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all visible satellites are used for location determination, then the quantity of satellites increases improving positioning availability, but multipath reflections from obstructed satellites degrade positioning accuracy
Solution Approach 1:
The patent applies dynamics by making the elevation mask adaptive rather than static. The system learns the local terrain and obstruction characteristics dynamically, adjusting the elevation mask thresholds based on actual environmental conditions. This allows the system to optimize satellite selection in real-time, excluding satellites blocked by local features while including those with clear paths, thereby resolving the contradiction between signal availability and reliability.
Solution Approach 2:
The system implements feedback through its learning mechanism that uses observed positioning residuals and signal quality metrics to continuously refine the elevation mask parameters. By feeding back the actual performance data from multiple positioning solutions, the system adapts the mask thresholds to local conditions, improving both accuracy and reliability simultaneously through iterative optimization.
2Measurement precision
If a static elevation mask is used to filter satellites, then processing complexity is reduced, but accuracy deteriorates due to inability to account for local terrain obstructions
Solution Approach 1:
The system applies preliminary action by pre-computing and storing the learned elevation mask parameters based on historical positioning data and known terrain information. This pre-processing allows the system to have accurate local obstruction models ready before actual positioning operations, reducing the computational burden during real-time location determination while maintaining high accuracy through pre-learned environmental characteristics.
3Measurement precision
If learned-elevation mask is generated using sensor data, then location accuracy improves by filtering non-line-of-sight satellites, but device complexity increases due to additional sensors and processing
Solution Approach 1:
The system applies self-service by using the device's own existing sensor data (accelerometers, gyroscopes, and other onboard sensors) to generate the learned elevation mask, rather than requiring external or additional specialized sensors. The system leverages data it already collects for other purposes, processing this information to infer local terrain characteristics and obstruction patterns, thereby improving accuracy without proportionally increasing hardware complexity.
Data Source
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AI summary
A method is provided for establishing a location of a device based on a global navigation satellite system. Methods may include: receiving sensor data of an environment of the apparatus; estimating object location within the environment based on the sensor data; receiving a static elevation mask; generating a learned-elevation mask based, at least in part, on the static elevation mask and the estimated object location within the environment; receiving signals from a plurality of Global Navigation Satellite System (GNSS) satellites; filtering the signals from the plurality of GNSS satellites to eliminate from consideration a subset of satellites established as not having a line-of-sight with the apparatus; establishing a location of the apparatus from remaining satellites established as having a line-of-sight with the apparatus; and providing for at least one of route guidance or autonomous vehicle control based on the established location of the apparatus.