Calibration Method for Relativistic Navigation System Using Parallel Q-Learning Extended Kalman Filter
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View abstract on PubMed
Summary
This summary is machine-generated.A novel parallel Q-learning extended Kalman filter (PQEKF) calibrates measurement bias in relativistic navigation systems. This method improves spacecraft positioning accuracy to under 300 meters in Medium Earth Orbit.
Area Of Science
- Spacecraft navigation
- Relativistic effects in astrodynamics
- Estimation theory
Background
- Relativistic navigation systems rely on precise measurements of phenomena like stellar aberration and gravitational light deflection.
- Inter-star angle measurement bias, stemming from star sensor misalignment, significantly degrades navigation accuracy.
- Existing methods struggle to effectively calibrate these biases in real-time.
Purpose Of The Study
- To introduce a novel parallel Q-learning extended Kalman filter (PQEKF) for measurement bias calibration in relativistic navigation.
- To enhance the accuracy of spacecraft position and velocity estimation by mitigating inter-star angle measurement errors.
- To automatically tune the filter's process noise covariance matrix using Q-learning.
Main Methods
- Development of a parallel Q-learning extended Kalman filter (PQEKF) algorithm.
- Integration of Q-learning for adaptive tuning of the process noise covariance matrix.
- Extraction of relativistic perturbations from inter-star angle measurements using high-accuracy star sensors.
Main Results
- The PQEKF effectively estimates spacecraft position, velocity, and measurement bias parameters.
- Numerical simulations in a Medium Earth Orbit (MEO) scenario demonstrate the method's high performance.
- Achieved positioning accuracy below 300 meters with inter-star angle measurement accuracy of approximately 1 milliarcsecond (mas) after calibration.
Conclusions
- The PQEKF offers a robust solution for measurement bias calibration in relativistic navigation.
- The adaptive nature of Q-learning enhances the filter's ability to handle dynamic noise characteristics.
- This approach significantly improves the overall navigation performance and reliability of spacecraft systems.
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