GNSS Substitute Correction Data Generation for Localization
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Solution Overview
Problem
Existing GNSS-based localization methods face challenges due to errors in propagation time measurements, particularly when correction data streams are interrupted, leading to inaccurate positioning, especially in autonomous driving applications.
Innovation Solution
A method for generating substitute correction data by recognizing impaired receipt of correction data, reading previously received data, and using it to extrapolate or interpolate current values, allowing for precise positioning even during data failures, utilizing adaptive mathematical approaches like Lagrange polynomials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correction data streams are used for GNSS-based localization, then positioning accuracy is improved, but the system becomes vulnerable to data stream interruptions and communication disturbances
Solution Approach 1:
The system stores correction data in advance before interruptions occur. When data stream interruptions are detected, the previously stored correction data is retrieved and used to generate substitute correction data, ensuring continuous operation without waiting for new data arrivals.
Solution Approach 2:
The system creates substitute correction data by copying and extrapolating from previously received correction data. This copying approach allows the system to maintain positioning accuracy during interruptions by reproducing correction values based on historical data patterns.
2Measurement precision
If correction data is continuously received and stored, then positioning precision during normal operation is improved, but memory usage and data management complexity increase
Solution Approach 1:
The system extracts only the essential correction data parameters needed for substitution during interruptions, rather than storing complete correction data sets. This extraction approach reduces memory requirements while maintaining the ability to generate accurate substitute correction data when needed.
Solution Approach 2:
The system changes the representation parameters of stored correction data, storing key parameters that enable extrapolation rather than complete data sets. This parameter transformation reduces storage requirements and simplifies data management while preserving the ability to reconstruct correction data during interruptions.
3Reliability
If extrapolation methods are used to generate substitute correction data, then positioning continuity is improved during data interruptions, but measurement uncertainty increases
Solution Approach 1:
The system ensures continuous positioning operation by generating substitute correction data through extrapolation when original data streams are interrupted. This continuity approach maintains useful positioning action without interruption, accepting controlled uncertainty increases as a trade-off for uninterrupted operation.
Solution Approach 2:
The system uses feedback from the interruption detection mechanism to trigger substitute correction data generation. When interruptions are detected, the feedback loop activates extrapolation methods to maintain positioning continuity, and when normal data flow resumes, the system switches back to using original correction data, reducing uncertainty.
Data Source
AI summary
A method for generating substitute correction data for GNSS-based localization of a mobile device is disclosed. The method includes: a) recognizing that receipt of correction data from at least one correction data source is currently impaired, b) reading of correction data that has been received earlier, and c) generating substitute correction data for the current situation by using at least part of the correction data that has been received earlier.