Satellite Orbit Prediction Using Ephemeris Error Correction
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Solution Overview
Problem
Global navigation satellite systems (GNSS) face challenges in accurately determining satellite orbits due to limited initial condition accuracy and computational constraints, leading to position errors that grow over time, especially when signals are interrupted by obstacles like buildings or trees.
Innovation Solution
A satellite orbit prediction method that stores past and recent ephemerides data, computes reference satellite positions using celestial mechanics force models, identifies position error functions by fitting curves to errors, and employs these functions to correct predicted satellite orbits, thereby improving accuracy and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous satellite orbit prediction is implemented in the receiver, then the receiver can maintain navigation capability during signal interruptions, but position errors grow over time due to limited initial condition accuracy
Solution Approach 1:
The receiver performs preliminary actions by storing multiple past ephemerides datasets before signal interruption occurs. These pre-stored datasets serve as the foundation for autonomous orbit prediction when signals are blocked, allowing the receiver to maintain navigation capability without real-time satellite data.
Solution Approach 2:
The system establishes a feedback mechanism by comparing predicted satellite positions with actual observed positions when signals are available. The position errors are calculated and used to generate correction terms that are applied to future predictions, continuously improving accuracy and compensating for the time-growth of errors.
2Measurement precision
If comprehensive force models are used for orbit prediction, then prediction accuracy improves, but computational complexity becomes prohibitive for portable devices
Solution Approach 1:
The invention extracts and separates the correction term calculation from the complete force model computation. By identifying and removing the dominant error sources (mainly related to Earth rotation and polar motion) as distinct correction terms, the system achieves high accuracy without implementing the full complexity of comprehensive force models in the portable device.
Solution Approach 2:
The system changes the computational approach by transitioning from direct computation of complex force models to a parameter-based correction method. The correction terms are derived from ephemeris data parameters, transforming the problem from solving differential equations to applying algebraic corrections, significantly reducing computational burden while maintaining accuracy.
3Measurement precision
If ephemeris data is downloaded continuously, then position accuracy is maintained, but signal interruptions cause data loss and require waiting for re-acquisition
Solution Approach 1:
The receiver performs preliminary data accumulation by storing multiple historical ephemerides datasets in its memory before signal interruption occurs. This pre-stored data serves as a reservoir that enables continuous autonomous prediction during blockages, eliminating the need to wait for signal re-acquisition to resume navigation.
Solution Approach 2:
The system prepares in advance by maintaining a buffer of past ephemerides data in memory, creating a cushion that protects against the harmful effect of signal interruptions. This pre-stored data buffer allows the prediction algorithm to continue operating autonomously without interruption, cushioning the system against the time loss that would otherwise occur during signal blockage.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enhances the accuracy and speed of satellite orbit prediction, reducing location uncertainty and enabling faster navigation by correcting position errors and accounting for physical effects like polar motion, even in real-world conditions with signal interruptions.
Implementation Method 1
The orbit of each satellite can be estimated by solving a classical celestial mechanics problem with an adequate level of knowledge about the overall forces (gravitational and non-gravitational) acting on the satellite
Implementation Method 2
The transformation between ECEF and ECI coordinate systems includes in theory a number of different physical effects, the so called precession, nutation, polar motion effects and sidereal time calculation
Data Source
AI summary
A satellite orbit prediction method of a satellite navigation system includes storing satellite past ephemerides data associated with past time intervals and satellite recent ephemeris data associated with a recent time interval, and computing reference satellite positions based on the past and recent ephemerides data. The method also includes: computing estimated satellite positions by propagating on the past time intervals using a celestial mechanics force model and the recent ephemeris data, computing errors from the reference satellite positions and estimate satellite positions, and identifying a position error function by fitting a curve to the computed errors. The position error function is used to obtain corrected predicted satellite orbit portions.