GNSS Rover-Engine Multi-Baseline Averaging for Decimeter Accuracy
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Solution Overview
Problem
Current Global Navigation Satellite Systems (GNSS) technologies face challenges in achieving decimeter-level positioning accuracy in a timely manner, particularly for long distances and in scenarios where reference or correction data is limited.
Innovation Solution
The implementation of a real-time GNSS rover-engine that employs advanced techniques such as interpolation and extrapolation of reference data, minimum-error combinations, forward filtering with noise whitening, and multi-baseline averaging to enhance positioning accuracy and reduce convergence time, along with a stochastic post-processing accuracy predictor to optimize data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional GNSS processing methods are used, then positioning accuracy can eventually reach decimeter level, but convergence time becomes excessively long (several times longer than desired)
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing ambiguity parameters and covariance matrices during static periods when the rover is stationary. These pre-computed values are then rapidly applied during movement periods, eliminating the need to re-converge ambiguities from scratch and dramatically reducing convergence time while maintaining decimeter-level accuracy
Solution Approach 2:
The patent implements dynamics by adapting the processing mode based on rover movement state. The system dynamically switches between static processing (for pre-computation) and kinematic processing (for rapid updates during movement), optimizing both accuracy and convergence time for different operational phases
2Loss of time
If reference data is interpolated and extrapolated to match rover data, then convergence time is reduced, but processing complexity increases
Solution Approach 1:
The patent uses interpolation and extrapolation as intermediary techniques to generate reference data at rover epochs when direct measurements are unavailable. This intermediary approach allows the system to maintain continuous processing and rapid convergence while managing complexity through established mathematical methods
3Measurement precision
If multi-baseline averaging is implemented, then positioning accuracy is enhanced for long distances, but computational requirements increase
Solution Approach 1:
The patent applies partial action by selectively using multiple baselines for averaging rather than processing all possible baseline combinations. The system computes positions from several different baselines and averages them, providing enhanced accuracy for long distances while limiting computational requirements through selective baseline selection
Data Source
AI summary
Methods and apparatus for processing of data from GNSS receivers are presented. (1) A real-time GNSS rover-engine, a long distance multi baseline averaging (MBA) method, and a stochastic post-processed accuracy predictor are described. (2) The real-time GNSS rover-engine provides high accuracy position determination (decimeter-level) with short occupation time (2 Minutes) for GIS applications. The long distance multi baseline averaging (MBA) method improves differential-correction accuracy by averaging the position results from several different baselines. This technique provides a higher accuracy than any single baseline solution. It was found, that for long baselines (more than about 250 km), the usage of non-iono-free observables (e.g. L1-only or wide-lane) leads to a higher accuracy with MBA compared to the commonly used iono-free (LC) combination, because of the less noisy observables and the cancellation of the residual ionospheric errors. (3) The stochastic post-processed accuracy (SPPA) predictor calculates during data collection an estimate of the accuracy likely to be achieved after post-processing. This helps to optimize productivity when collecting GNSS data for which post-processed accuracy is important. The predictor examines the quality of carrier measurements and estimates how well the post-processed float solution will converge in the time since carrier lock was obtained.