Application of adaptive EKF in real-time orbit determination

Abstract

High-precision orbits are essential for low Earth orbit (LEO) satellite applications. Instead of determinating orbit by the ground monitoring stations, LEO missions are trending to incorporate spaceborne global positioning system receivers for autonomous navigation. Focusing on this issue, we propose a method of real-time orbit determination based on ionosphere combination observation of dual-frequency data using adaptive extended Kalman filter (AEKF). The traditional Kalman filter heavily relies on the initial values of measurement and process noise covariance matrix. However, the environment of the satellite is time-variant, and it is hard to keep the error covariance matrix in the optimal condition all the time. To overcome this disadvantage, the Sage–Husa Kalman filter is introduced. The performance of the proposed algorithm is compared with the conventional EKF, and the results show that the proposed method can guarantee the precision and stability of orbit determination. With the proposed algorithms, we developed a software to solve the orbit suitable for one-day Gravity Recovery and Climate Experiment (GRACE) observations. The obtained results show that the accuracy of orbit determination error is improved with unknown Kalman process noise compared with traditional method. Additionally, the performances of AEKF for different gravity field models are analyzed and compared.