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Summary
This summary is machine-generated.

Accurate satellite tracking is vital for space safety. A new Bayesian Adaptive Extended Kalman Filter (BAEKF) improves orbit determination accuracy by 34.7% over traditional methods, enhancing space situational awareness.

Keywords:
Bayesianextended Kalman filteroptical observationsorbit determination

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Area of Science:

  • Aerospace Engineering
  • Astrodynamics
  • Space Situational Awareness

Background:

  • Increasing satellite numbers and space debris in Low-Earth Orbit (LEO) necessitate precise orbit determination.
  • Ground-based optical observations face limitations from data constraints and noise, challenging traditional Extended Kalman Filter (EKF) accuracy.

Purpose of the Study:

  • To develop a more accurate and robust method for high-precision orbit determination using optical observations.
  • To overcome the limitations of the Extended Kalman Filter (EKF) in complex orbital environments.

Main Methods:

  • Proposed a Bayesian Adaptive Extended Kalman Filter (BAEKF).
  • BAEKF optimizes track determination via dynamic noise covariance adjustment and Bayesian a posteriori probability correction.
  • Evaluated performance against traditional EKF, Unscented Kalman Filter (UKF), Radial Basis Function Neural Network (RBFNN), and Gaussian Process Regression (GPR).

Main Results:

  • BAEKF demonstrated a 34.7% average reduction in root mean square error (RMSE) compared to the traditional EKF.
  • Significant improvements in accuracy and stability were observed for BAEKF in nonlinear systems.
  • UKF, RBFNN, and GPR also showed performance enhancements.

Conclusions:

  • The proposed BAEKF offers a reliable solution for high-precision orbit determination from optical observations.
  • BAEKF effectively addresses the accuracy and stability issues inherent in traditional EKF for complex space environments.
  • This advancement contributes to safer spacecraft operations and enhanced space situational awareness.