Adaptive Atmospheric Correction Model Optimization
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Solution Overview
Problem
GNSS-based navigation systems face significant positional accuracy issues due to atmospheric delays and advances, particularly in urban areas where abnormal atmospheric activity, such as solar geomagnetic storms, cannot be sufficiently modeled, leading to decreased precision in navigation device positioning.
Innovation Solution
An atmospheric abnormality mitigation system dynamically adjusts atmospheric delay correction models by generating prediction modeling data using various atmospheric activity models and comparing it with real-time observation data to identify and mitigate the effects of abnormal atmospheric activity, thereby improving positional accuracy and reducing computational resources and data transmission inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional atmospheric delay correction models are used, then computational resources and data transmission are reduced, but positional accuracy deteriorates during abnormal atmospheric activity
Solution Approach 1:
The patent implements dynamic adjustment of correction model parameters based on atmospheric conditions. The system transitions from static correction models to dynamic models that adapt their complexity and update frequency according to real-time atmospheric stability indicators, allowing higher accuracy during abnormal conditions while maintaining efficiency during normal conditions
Solution Approach 2:
The system changes key parameters including correction model selection, update frequency, and grid resolution based on atmospheric conditions. During abnormal atmospheric activity, the system switches to more complex correction models with higher update frequencies and finer spatial resolution, while using simpler models during stable conditions
2Measurement precision
If correction data is updated frequently to maintain accuracy during abnormal atmospheric activity, then positional accuracy improves, but data transmission and computational resources increase
Solution Approach 1:
The system implements periodic correction data updates with variable periods based on atmospheric conditions. During stable atmospheric conditions, updates occur at standard intervals, while during abnormal conditions, the update frequency increases dynamically. This allows the system to maintain accuracy when needed while minimizing unnecessary transmissions during stable periods
Solution Approach 2:
The system uses feedback from atmospheric monitoring data to dynamically adjust correction data update frequency. When atmospheric parameters indicate instability or abnormal conditions, the system automatically increases update frequency. This feedback mechanism ensures accuracy improvement during critical periods while avoiding excessive transmissions during stable conditions
3Adaptability or versatility
If standard atmospheric correction models are used, then data transmission efficiency is maintained, but the system cannot sufficiently address abnormal atmospheric activity such as solar geomagnetic storms
Solution Approach 1:
The system performs self-diagnosis by monitoring atmospheric conditions and automatically determines when standard correction models are insufficient. It autonomously switches to enhanced correction modes or alternative models when abnormal conditions are detected, without requiring external intervention or pre-programmed response to specific atmospheric events
4Measurement precision
If low-cost receivers correct for fewer errors, then device complexity is reduced, but positional accuracy deteriorates due to uncorrected atmospheric delays
Solution Approach 1:
The system introduces an intermediary correction data processing layer between the satellite signals and the receiver positioning calculation. This intermediary layer provides pre-computed atmospheric corrections that enhance accuracy without requiring complex processing within the receiver itself, allowing low-cost receivers to achieve higher accuracy through externally provided correction data
Data Source
AI summary
A method, apparatus and computer program product are configured to adaptively adjust atmospheric delay correction data transmitted to a navigation device based on identified atmospheric abnormalities. A method generates atmospheric prediction modeling data and receives observation data including data related to current atmospheric activity associated with a particular geographical area. The method compares, such as by utilizing an atmospheric activity comparison algorithm, the prediction modeling data and the observation data to determine whether an atmospheric abnormality is adversely affecting one or more navigational signals associated with the particular geographical area. An atmospheric delay correction model associated with a service provider is updated by executing one or more reconfiguration actions.