GNSS Visibility Prediction for Urban Aircraft Navigation
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Solution Overview
Problem
GNSS-based navigation in obstacle-dense environments, such as cities, is challenged by satellite signal attenuation from buildings and trees, leading to unreliable positioning and navigation data for aircraft, particularly unmanned or highly automated vehicles.
Innovation Solution
A system and method that predicts satellite visibility by combining satellite orbit data and elevation data to generate predictions of satellite visibility throughout a geographical region, using GPUs for parallel processing to determine satellite combinations and quality, enabling safe flight planning and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If aircraft operate in urban environments with buildings and trees, then flight routes can access more locations and reduce travel time, but satellite signal attenuation increases leading to unreliable GNSS positioning
Solution Approach 1:
The system performs preliminary calculations of satellite visibility and GNSS performance quality before flight operations. By pre-computing which satellites will be visible at specific locations and times using terrain data and satellite orbit data, the system enables flight planners to identify reliable GNSS coverage areas in advance, allowing aircraft to operate safely in urban environments with predictable navigation performance.
2Device complexity
If traditional GNSS navigation is used in obstacle-dense environments, then the navigation system remains simple, but satellite signals are blocked leading to navigation failures
Solution Approach 1:
The system pre-calculates satellite visibility and GNSS performance metrics before flight operations by processing terrain elevation data and satellite orbit data. This preliminary action creates a database of expected GNSS performance at various locations and times, enabling the navigation system to rely on pre-computed information rather than requiring complex real-time signal processing during flight.
Solution Approach 2:
The system introduces an intermediary computational layer that processes terrain data and satellite orbit data to generate GNSS performance quality information. This intermediary system acts as a mediator between the physical environment (obstacles blocking signals) and the navigation system, providing processed information about satellite visibility without requiring the aircraft's navigation system to directly handle complex signal processing.
3Reliability
If real-time satellite signal monitoring is implemented, then navigation reliability improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs all satellite visibility and GNSS performance calculations in advance, before flight operations begin. By pre-computing which satellites will be visible at specific locations and times using terrain data and satellite orbit data, the system eliminates the need for time-consuming real-time calculations during flight, while still providing reliable navigation information.
Data Source
AI summary
A method for global navigation satellite system (GNSS)-based navigation may include retrieving terrain data for a geographical region and satellite orbit data for a GNSS comprising a plurality of satellites. Then, for of a plurality of time steps in a time period and for each of a plurality of lateral positions in the geographic region, a minimum height at which each satellite is visible from the lateral position at the time step may be determined. Unique combinations of satellites visible from the lateral position at the time step may be identified and error objects indicating GNSS performance quality corresponding to the unique combination of satellites may be generated. Performance quality information may be produced based on the one or more unique combinations of satellites and the respective one or more error objects. At least one aircraft may be operated based on this performance quality information.