Tracking Station Observation Value Prediction for RTK and PPP
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Solution Overview
Problem
The accuracy of satellite positioning systems, such as GPS and GLONASS, is affected by satellite orbit errors, clock bias errors, and atmospheric propagation errors, which can lead to reduced positioning performance in applications requiring centimeter-level accuracy like surveying, precision agriculture, and intelligent driving.
Innovation Solution
A method and device for predicting the observation value of a tracking station by determining variations in geometrical distance, tropospheric error, ionospheric error, and satellite clock bias between epochs, allowing for the estimation of current observation values even when data cannot be used in data processing due to network or receiver issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from tracking stations is used in real-time processing, then positioning accuracy is improved, but network reliability and data transmission stability become critical issues
Solution Approach 1:
The system pre-calculates and stores prediction models for satellite orbit errors, clock bias errors, and atmospheric propagation errors before real-time processing. When tracking station data is unavailable, these pre-established models enable immediate prediction without waiting for data transmission, thus maintaining positioning accuracy while eliminating network dependency.
Solution Approach 2:
Instead of directly using real-time tracking station observation values that may be lost during transmission, the system creates predictive copies of these values through mathematical models. The prediction module generates estimated observation values that replicate the function of actual tracking data, ensuring continuous operation even when original data is unavailable.
2Area of stationary object
If multiple physical base stations are built to form a base station network, then coverage area and positioning availability are improved, but system complexity and construction cost increase
Solution Approach 1:
The prediction module acts as an intermediary between limited physical base stations and the broader service area. By using prediction models to estimate observation values, the system extends the effective coverage of existing base stations without requiring physical expansion of the base station network, thus maintaining wide coverage while reducing system complexity.
Solution Approach 2:
The system changes the parameter of base station density by introducing virtual base stations through prediction. Instead of increasing the physical number of base stations, the prediction module generates virtual observation data that effectively increases network density and coverage area without additional physical infrastructure.
3Duration of action of stationary object
If prediction of observation values is implemented when data is unavailable, then positioning service continuity is improved, but prediction accuracy may be compromised
Solution Approach 1:
The system applies partial prediction action by using prediction models only for the specific error components (orbit errors, clock bias, atmospheric errors) that can be accurately modeled. For other components requiring actual observation data, the system waits for real data arrival, thus maintaining service continuity for predictable errors while preserving accuracy for unpredictable components.
Solution Approach 2:
The prediction system incorporates feedback mechanisms where actual tracking station data, when available, is used to validate and refine prediction models. This feedback loop continuously improves prediction accuracy while maintaining service continuity, as the system learns from actual data patterns to enhance future predictions without compromising real-time positioning service.
Data Source
AI summary
This application discloses a method and a device for realizing prediction of an observation value of a tracking station. Based on a static tracking station, in a case that data of the tracking station does not arrive at a processing center accurately and timely, by using an observation value at a reference epoch of the tracking station and variations between the reference epoch and a current epoch, an observation value at a moment of the current epoch of the tracking station is predicted. In this way, an accurate observation value that can be used for data processing of PPP or network RTK can be predicted, so that positioning performance of the PPP and the RTK for serving users is improved.