BeiDou Satellite Observation Data Correlation Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The BeiDou Navigation Satellite System (BDS) is susceptible to errors such as ionospheric delay, troposphere delay, and multipath, which can affect positioning accuracy, and existing Ground-Based Augmentation Systems (GBAS) do not adequately address issues in observation data correlation, potentially leading to satellite failure detection delays and compromising user safety.
Innovation Solution
A correlation processing method that collects and classifies observation data from BDS, uses a Lagrange function to determine data reliability, performs stationarity tests, and builds an observation model to improve data quality and detect satellite failures, thereby enhancing the integrity and reliability of BDS services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GBAS processes observation data without correlation analysis, then processing speed is maintained, but data quality and reliability deteriorate due to undetected satellite failures and correlated errors
Solution Approach 1:
The patent performs correlation analysis and stationarity tests on observation data before using it for positioning calculations. By conducting these preliminary checks, the system identifies and removes correlated data points that would otherwise compromise positioning accuracy, thereby improving reliability without significantly increasing overall system complexity
Solution Approach 2:
The patent introduces an intermediate processing layer between raw observation data collection and final positioning computation. This intermediary layer performs correlation analysis, stationarity testing, and data quality assessment, acting as a mediator that filters out unreliable data before it affects positioning results, thus enhancing data quality while maintaining a modular system structure
2Reliability
If correlation analysis and stationarity tests are performed on all observation data, then data credibility improves, but processing time increases
Solution Approach 1:
The patent applies correlation analysis and stationarity tests selectively to specific portions of observation data rather than uniformly to all data points. By identifying and focusing computational resources on data segments that exhibit correlation or non-stationarity characteristics, the system improves data credibility where needed while minimizing unnecessary processing time for already reliable data
Solution Approach 2:
The patent dynamically adjusts processing parameters based on initial data quality assessments. When observation data shows signs of correlation or non-stationarity, the system intensifies analysis on those specific data segments; when data appears reliable, it reduces processing intensity, thereby optimizing the balance between data credibility improvement and processing time consumption
3Measurement precision
If satellite failure detection is delayed, then system continuity is maintained, but positioning accuracy deteriorates due to corrupted observation data
Solution Approach 1:
The patent implements a feedback mechanism where correlation analysis results and stationarity test outcomes are continuously monitored and fed back into the satellite status assessment process. When correlated errors or non-stationarity are detected in observation data, the system immediately flags potential satellite failures and adjusts positioning calculations accordingly, thereby improving both positioning accuracy and failure detection reliability through continuous feedback loops
Data Source
AI summary
A correlation processing method based on observation data from the Deidou satellite includes steps of: a) collecting observation data from the BeiDou Navigation Satellite System; b) capturing data samples according to the observation data, and determining a membership degree of each data sample, and the data samples are classified; c) constructing a target classification function, and selecting the data samples to remove the unavailable data samples; and testing stationarity of each time series; d) building an observation model by using data samples having stationarity in step c), and determining correctness of the observation model and optimizing the observation model if the observation model is not correct; and e) searching a correct observation model obtained in step d) for correlation data, and performing correlation analysis of the observation data. The method of the present invention improved the quality of observation data can be effectively improved.