Predicted Orbit Determination Performance of a Lunar Navigation System: Utilizing Inter-Satellite Measurements with Batch and EKF Estimation

In this paper, we propose a practical two-step approach for orbit determination by utilizing Batch estimation to provide initial values for an Extended Kalman Filter (EKF)-based recursive estimation. This method allows for more accurate and stable orbit determination by combining the strengths of both techniques. First, Batch estimation provides an initial estimate, and then EKF refines this estimate through continuous error modeling by recursive measurement updates in dynamic environments. We present simulation results demonstrating the effectiveness of this approach, particularly in improving orbit determination accuracy for lunar orbital users. The simulation consists of two parts: first, the actual trajectory of the lunar orbital user is generated using the General Mission Analysis Tool (GMAT), and then this trajectory is input into a Matlab-based measurement generation program to obtain observation data of lunar orbital user. The observation data is then input into the Matlab-based orbit determination simulation program, where the orbital filter used for orbit determination is set to have a lower accuracy than the orbit propagator used when generating the true trajectory in GMAT.