Adaptive Electron Density Model for GNSS Ionospheric Correction
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Solution Overview
Problem
Current ionospheric models for single-frequency GNSS receivers suffer from inhomogeneous station distribution, inhomogeneous data quality, and significant loss of information due to model smoothing, leading to coarse resolution and data gaps, which affect the accuracy of ionospheric corrections.
Innovation Solution
An adaptive model of electron density distribution is determined using local electron density data from both stationary and movable provision points, with dynamic redistribution of basis functions to achieve spatial and temporal adaptivity, allowing for high-resolution modeling in dense areas and bridging data gaps with a combination of local and global models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If globally defined basis functions (spherical harmonics or voxels) are used for TEC parameterization and interpolation, then a homogeneous model structure is achieved, but the resolution degrades due to model smoothing in areas with dense station distribution and data gaps in areas without coverage
Solution Approach 1:
The patent applies local quality by using locally defined basis functions instead of globally defined ones. Each local region has its own basis functions adapted to the local station distribution characteristics, allowing high resolution in dense areas (like Europe) while maintaining stability in sparse areas. This resolves the contradiction by making the model structure locally optimized rather than globally uniform.
Solution Approach 2:
The patent segments the global ionosphere model into multiple local regions, each modeled independently with its own basis functions. This segmentation allows different resolution levels in different regions, preventing the smoothing effect that occurs when a single global model must accommodate both dense and sparse station distributions.
2Manufacturing precision
If the degree and order of KFF or voxel sizes are defined by the station distribution, then areas with dense observations can achieve high resolution, but areas without station coverage must be bridged with very coarse model resolution
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the basis function parameters (degree, order, voxel sizes) to be dynamically adjusted based on local station distribution density. In dense areas, higher resolution parameters are used; in sparse areas, the model automatically adapts to use appropriate coarser parameters while maintaining continuity through the local basis function formulation.
3Area of stationary object
If stationary reference stations with inhomogeneous distribution are used to determine TEC values, then global coverage is attempted, but significant information loss occurs due to model smoothing and data gaps in uncovered areas
Solution Approach 1:
By using locally defined basis functions centered at each reference station, the patent ensures that each station's information is utilized optimally in its local region without being smoothed out by global interpolation. This local quality approach preserves detailed ionosphere information in covered areas while the piecewise construction ensures continuity across the global domain.
Solution Approach 2:
The patent creates local copies of the basis function model at each reference station location. Each local model is a copy adapted to its specific location's station distribution, allowing information to be preserved locally rather than being lost to global smoothing. These local copies are then assembled to form the complete global model.
Data Source
Figure 1~2
AI summary
The invention relates to a method for determining a model of an electron distribution in the Earth's atmosphere, which is used to correct propagation time measurements of signals emitted by earth satellites for position determination operations using signal receivers, having at least the following steps of: a) determining local electron density data relating to provision locations (2), b) determining a local resolution accuracy on the basis of a local density of provision locations, c) determining functions for interpolating the electron density distribution determined in step a) on the basis of the resolution accuracy determined in step b), d) creating the model of the electron density distribution using the data determined in step a) and the functions determined in step c).