Adaptive Ionospheric Correction Grids for Abnormal GNSS Conditions
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Solution Overview
Problem
Existing satellite-based navigation systems face significant positional inaccuracies due to ionospheric delays and advances, which are not adequately addressed by current ionospheric correction models, especially during abnormal atmospheric conditions such as solar geomagnetic storms, leading to decreased precision in navigation devices like smartphones and vehicles.
Innovation Solution
An adaptive atmospheric abnormality mitigation system that monitors ionospheric activity using multiple grids and machine learning models to dynamically adjust correction data transmission rates and grid layouts in response to abnormal atmospheric conditions, providing real-time updates and warnings to navigation devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single static correction grid is used, then device complexity is reduced, but measurement precision deteriorates during abnormal atmospheric conditions
Solution Approach 1:
The correction system is divided into multiple static grids covering different geographical regions, each with pre-calculated ionospheric correction parameters. The receiver selectively applies corrections from relevant grids based on its location, achieving high precision without requiring a single complex global model.
Solution Approach 2:
The system dynamically selects and combines corrections from multiple static grids based on the receiver's current position and the active atmospheric conditions. This dynamic selection mechanism allows the system to adapt to abnormal atmospheric conditions in specific regions while maintaining simplicity in each individual grid definition.
2Measurement precision
If multiple correction grids are implemented, then measurement precision improves, but loss of information increases due to data management complexity
Solution Approach 1:
Correction parameters for multiple grids are pre-calculated and stored in the receiver memory before operation. This preliminary preparation eliminates the need for real-time computation and complex data management during operation, as the receiver simply retrieves and applies the appropriate pre-computed corrections based on its current location.
3Reliability
If real-time ionospheric monitoring is implemented, then reliability improves during abnormal conditions, but use of energy increases
Solution Approach 1:
Ionospheric correction parameters are pre-calculated and stored for multiple static grids covering different regions and atmospheric conditions. The receiver retrieves these pre-computed parameters based on its location and the current atmospheric state indicator, avoiding continuous real-time computation and reducing energy consumption while maintaining high reliability during abnormal conditions.
4Measurement precision
If correction data is transmitted frequently, then measurement precision is maintained during abnormal activity, but productivity of data transmission decreases
Solution Approach 1:
The system uses an atmospheric state indicator to trigger periodic updates of correction parameters only when abnormal conditions are detected. During normal conditions, corrections are transmitted at standard intervals, while during abnormal conditions, updates are triggered more frequently to maintain precision, optimizing the balance between accuracy and transmission efficiency.
Data Source
AI summary
A method, apparatus and computer program product are configured to adaptively adjust ionospheric delay correction grids and data transmitted to a navigation device based on identified ionospheric abnormalities.