GNSS Observation Preprocessing for Gross Error and Cycle Slip Detection
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Solution Overview
Problem
Existing observation data preprocessing methods in GNSS systems fail to accurately detect gross errors or cycle slips, leading to invalid processing results.
Innovation Solution
A data preprocessing method utilizing fuzzy cluster analysis and QR decomposition to identify inaccurate observation values, adjusting weight values, and iteratively refining satellite information to enhance detection efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional observation data preprocessing methods are used, then the processing process is simple, but the detection accuracy of gross errors or cycle slips is insufficient
Solution Approach 1:
The patent divides the detection process into multiple independent stages: obtaining observation data, calculating residuals, performing QR decomposition, conducting cluster analysis, and adjusting weight values. Each stage processes specific information independently, making the complex detection task manageable while improving accuracy through systematic analysis of multiple data dimensions
Solution Approach 2:
The patent introduces multiple analysis dimensions by computing both check vectors and eigenvectors from the residual data, then performing cluster analysis in this expanded feature space. This dimensional transformation enables the system to detect gross errors and cycle slips more accurately by analyzing patterns across multiple dimensions rather than relying on single-threshold methods
2Productivity
If traditional preprocessing methods are used, then the processing speed is fast, but the detection efficiency of multiple pieces of observation data with gross errors or cycle slips is insufficient
Solution Approach 1:
The patent merges the detection of multiple observation data pieces with gross errors or cycle slips into a unified cluster analysis process. By combining residual vectors and performing cluster analysis simultaneously across all observation data, the system efficiently identifies multiple erroneous data points in one operation rather than processing them separately, thereby improving both efficiency and reliability
Solution Approach 2:
The patent implements an iterative feedback mechanism where the weight values of navigation satellites are adjusted based on the cluster analysis results. The system uses the detected inaccurate observation data to modify subsequent processing iterations, continuously improving detection reliability while maintaining efficient processing speed through feedback-driven optimization
Data Source
AI summary
A method includes obtaining satellite information, including a plurality of navigation satellite identifiers, and satellite data, observed values of an observation parameter, and weight values that are separately associated with the plurality of navigation satellite identifiers; determining that an inaccurate observed value exists in a set of the observed values; determining, based on the satellite information, a check vector representing accuracy of the set, and a plurality of eigenvectors representing accuracy of an observed value of the observation parameter that is associated with a corresponding navigation satellite identifier; performing cluster analysis on the check vector and the plurality of eigenvectors, and determining a target navigation satellite identifier corresponding to an eigenvector that is of a same type as the check vector; and performing selection in the plurality of navigation satellite identifiers and/or adjusting a weight value associated with the target navigation satellite identifier based on the target navigation satellite identifier.