# Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites

## TL;DR

This paper introduces a new filter method to improve orbit determination accuracy for satellites using optical observations.

## Contribution

A Bayesian Adaptive Extended Kalman Filter (BAEKF) is proposed to enhance orbit determination accuracy and stability.

## Key findings

- BAEKF reduces RMSE by 34.7% compared to traditional EKF.
- BAEKF improves accuracy and stability in nonlinear orbital systems.
- UKF, RBFNN, and GPR also show improved RMSE values.

## Abstract

As the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc-segment angular data and dynamic noise interference, and the traditional Extended Kalman Filter (EKF) struggles to meet the accuracy and robustness requirements in complex orbital environments. To address these challenges, this paper proposes a Bayesian Adaptive Extended Kalman Filter (BAEKF), which synergistically optimizes track determination through dynamic noise covariance adjustment and Bayesian a posteriori probability correction. Experiments demonstrate that the average root mean square error (RMSE) of BAEKF is reduced by 34.7% compared to the traditional EKF, effectively addressing EKF’s accuracy and stability issues in nonlinear systems. The RMSE values of UKF, RBFNN, and GPR also show improvement, providing a reliable solution for high-precision orbital determination using optical observation.