GNSS Forecasting via Multi-Resolution 3D Modeling
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Solution Overview
Problem
Existing GNSS receivers with non-directional antennas face challenges in accuracy due to signal obscuration and multipath interference, particularly in urban and rural environments, which can lead to positioning errors and malfunctions in autonomous vehicles and drones.
Innovation Solution
The technology involves a cloud-based system that provides predictive data for GNSS receivers, using high-definition 3D maps and satellite orbit data to forecast line-of-sight visibility to individual satellites, allowing for satellite selection and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a non-directional antenna is used in GNSS receivers, then the device complexity is reduced and ease of operation is improved, but measurement precision deteriorates due to signal obscuration and multipath interference
Solution Approach 1:
The system performs preliminary actions by forecasting satellite visibility and multipath interference conditions before the receiver operates in a given location. The forecast engine pre-calculates which satellites will be visible and identifies potential multipath sources, allowing the receiver to prepare appropriate processing strategies in advance, thereby improving positioning accuracy in challenging environments.
Solution Approach 2:
A forecast engine acts as an intermediary between the satellite constellation and the GNSS receiver. This intermediary provides advance information about satellite visibility and multipath conditions, enabling the receiver to better process signals by knowing in advance which satellites will be observable and where multipath interference may occur, thus improving measurement precision without requiring complex directional antennas.
2Measurement precision
If predictive data and satellite selection are implemented, then measurement precision is improved, but device complexity increases due to cloud-based forecasting system
Solution Approach 1:
The forecast engine operates autonomously to generate satellite visibility and multipath forecasts without requiring manual configuration or complex processing at the receiver end. The system self-services by automatically calculating forecasts based on satellite ephemeris data and environmental models, then delivering these forecasts to receivers through standard communication channels, thereby improving precision while keeping individual receiver devices simple.
Solution Approach 2:
The patent replaces complex mechanical or hardware-based solutions (such as directional antennas or complex signal processing hardware) with a software-based forecast system. By substituting physical complexity with computational forecasting, the system achieves improved measurement precision while maintaining relatively simple receiver hardware and reducing overall system complexity through virtualization and cloud-based processing.
3Reliability
If cloud-based forecast system is used, then reliability is improved through better satellite selection, but loss of time occurs during data transmission and processing
Solution Approach 1:
The system performs preliminary actions by generating satellite visibility and multipath forecasts in advance, before the receiver needs to acquire position data. By pre-calculating which satellites will be visible and identifying potential interference sources, the system eliminates the need for time-consuming real-time analysis, thereby improving positioning reliability while minimizing time loss through proactive rather than reactive processing.
Solution Approach 2:
The forecast engine performs preliminary calculations of satellite visibility and multipath conditions before the actual positioning operation. This advance preparation allows receivers to immediately utilize pre-computed forecast data when needed, improving reliability by ensuring accurate satellite selection while reducing time loss since the forecasts are already available rather than requiring real-time computation during positioning operations.
Data Source
AI summary
Disclosed is representing distant objects for analysis of satellite line-of-sight visibility from a grid of points by constructing a first 3D model of foreground objects that obscure line-of-sight visibility of satellites from a grid of points, wherein the first 3D model is at a first resolution, where spacing of grid points denotes obstruction edges, constructing a second 3D model of background objects that are more than a threshold distance away and that object obscure line-of-sight visibility of satellites from the grid of points, wherein the second 3D model is at a second resolution that is different from and coarser than the first resolution, calculating a line-of-sight visibility of the satellites from the grid of points using a combination of the first and second 3D models, and responding to a query for an area by providing the calculated line-of-sight visibility of the satellites for points of the grid within the area.