Satellite Clock Error Forecasting With Sliding Frequency Outlier Filtering
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Solution Overview
Problem
Conventional satellite clock error forecast methods are limited by long data accumulation requirements, lack of real-time performance, and vulnerability to anomalous data, leading to reduced accuracy and reliability in navigation systems.
Innovation Solution
A real-time sliding ultrashort-term forecast model algorithm that converts satellite clock error phase data into frequency data, employs a frequency anomaly detection function to eliminate outliers, and performs a real-time sliding clock error forecast using fitted phase data and threshold ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical analysis or mathematical modeling methods are used for satellite clock error forecast, then forecast results can be provided, but real-time performance is insufficient and data accumulation requires a long period
Solution Approach 1:
The patent applies a sliding window mechanism that dynamically updates the forecast model with recent historical data, replacing the need for long-term static data accumulation. The model continuously adapts to changing satellite clock error characteristics by incorporating only the most recent N epochs of data, enabling real-time forecasting without requiring extended data accumulation periods.
Solution Approach 2:
The patent divides the historical data into discrete epochs and uses a sliding window approach to process only the most recent N epochs at a time. This segmentation allows the system to maintain real-time performance by focusing computation on a manageable subset of recent data rather than processing entire historical datasets, thus reducing data accumulation time requirements.
2Reliability
If conventional forecast methods are used, then forecast results can be obtained, but the methods are vulnerable to anomalous data and environmental changes, reducing reliability
Solution Approach 1:
The patent implements a feedback mechanism where the forecast model continuously monitors the impact of historical data on current predictions. When anomalies are detected in the sliding window data, the system adjusts the window parameters or excludes affected epochs, allowing the model to adapt to changing environmental conditions and maintain reliability despite the presence of anomalous data.
Solution Approach 2:
The sliding window approach dynamically adjusts the data processing scope based on current conditions. By continuously updating which historical epochs are included in the forecast calculation, the system can respond to environmental changes and exclude anomalous data points, thereby maintaining forecast reliability without being permanently affected by past anomalies.
3Adaptability or versatility
If long-term historical data is used for forecast modeling, then more comprehensive patterns can be captured, but real-time processing capability is reduced
Solution Approach 1:
The patent segments historical data into discrete epochs and uses a sliding window to process only the most recent N epochs for each forecast. This segmentation strategy maintains adaptability by incorporating recent data patterns while significantly reducing the computational burden compared to processing entire historical datasets, thereby improving real-time processing speed.
Solution Approach 2:
The sliding window mechanism dynamically adjusts the data scope based on real-time requirements. The window slides forward through historical data, continuously updating the forecast with the most recent information. This dynamic approach ensures the model adapts to current conditions while maintaining efficient processing speeds by avoiding reprocessing of entire historical datasets.
Data Source
AI summary
The present application relates to the field of satellite navigation systems, and discloses a real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data. The real-time sliding ultrashort-term forecast model algorithm based on frequency data and phase data includes: S1, converting clock error phase data into the frequency data; S2, processing the frequency data through a frequency anomaly detection function, and eliminating an abnormal value, where the frequency anomaly detection function determines whether the frequency data is an outlier according to a standard deviation and a threshold of the frequency data; and S3, performing a real-time sliding clock error forecast, and eliminating the abnormal value and updating a forecast epoch by using fitted phase data and a threshold range.