Adaptive Ionospheric Delay Model for GNSS Positioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current satellite navigation systems face challenges in accurately determining ionospheric delays, which affect the precision of position determination due to the dynamic and turbulent nature of the ionosphere, especially when using multi-frequency and multi-system receivers.
Innovation Solution
An adaptive algorithm adjusts the parameters of the ionospheric dynamic model in real-time based on the current ionospheric state, using recursive estimation and correlation analysis to improve the accuracy of ionospheric delay estimation and carrier phase ambiguity resolution across various processing modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed ionospheric delay model is used, then the system complexity is low, but the positioning precision deteriorates under dynamic ionospheric conditions
Solution Approach 1:
The patent implements a dynamic ionospheric delay model where parameters such as vertical total electron content (VTEC) and its gradient are continuously updated in real-time based on observed signal delays from multiple GNSS satellites. This allows the model to adapt to changing ionospheric conditions, resolving the contradiction between model complexity and positioning precision by making the model dynamic rather than static.
Solution Approach 2:
The patent changes the parameters of the ionospheric delay model adaptively by adjusting VTEC and gradient values based on real-time measurements. The model parameters are modified according to the current ionospheric state, allowing precise positioning even under dynamic conditions while maintaining reasonable model complexity through parameter adaptation rather than structural complexity.
2Measurement precision
If real-time adaptive adjustment of ionospheric parameters is implemented, then the positioning precision is improved, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on estimating only the most critical ionospheric parameters (VTEC and gradient) at specific locations along the satellite-receiver path. Rather than computing all possible ionospheric characteristics globally, the method concentrates calculations on locally relevant parameters, improving estimation accuracy while controlling computational complexity.
Solution Approach 2:
The patent uses partial action by implementing adaptive adjustment only when ionospheric conditions warrant it, rather than continuously updating all parameters at full resolution. The system selectively adjusts parameters based on observed variability, achieving sufficient precision without the full computational burden of continuous complete parameter re-estimation.
3Reliability
If multiple GNSS systems and frequencies are used, then the robustness against ionospheric disturbances is improved, but the difficulty of detecting and measuring ionospheric delays increases
Solution Approach 1:
The patent merges measurements from multiple GNSS systems (GPS, GLONASS, Galileo, BeiDou) and multiple frequency bands into a unified ionospheric delay estimation model. By combining observations across different systems and frequencies, the method improves robustness against ionospheric disturbances while managing measurement complexity through integrated processing rather than separate analyses.
Solution Approach 2:
The patent uses ionospheric pierce points and mapping functions as intermediaries to translate multi-system, multi-frequency observations into unified ionospheric delay estimates. These intermediaries facilitate the integration of diverse measurements by providing a common framework for comparing and combining data from different GNSS systems and frequencies, reducing the difficulty of detection and measurement.
Data Source
AI summary
A plurality of GNSS satellite signals feeds the signal processing engine operating in certain processing mode including carrier phase smoothed pseudorange positioning, precise point positioning (PPP), pseudorange differential (DGNSS), and carrier phase differential (RTK). The processing engine calculates two estimates of the ionosphere delay for each satellite: the filtered delay and the instant delay. Comparison of them allows to detect turbulent variation of the ionosphere and adjust parameters of two-parametric dynamic mode which improves positioning precision.