Simultaneous Mean and Covariance Correction Filter for Orbit Estimation.

Area of Science:

• Astrodynamics
• Spacecraft Navigation
• Estimation Theory

Background:

• Conventional nonlinear estimation schemes (NESs) for orbit determination face challenges with intractable nonlinear perturbation integrals (INPI).
• Accurate orbit state estimation is crucial for space mission success and requires robust handling of perturbations.

Purpose of the Study:

• To propose a novel filtering design for improved orbit state estimation of space targets.
• To address the limitations of existing nonlinear estimation schemes by employing an identification approach.

Main Methods:

• Modeling nonlinear perturbations as unknown inputs (UI) coupled with the orbit state, avoiding INPI.
• Developing a simultaneous mean and covariance correction filter (SMCCF) using a two-stage expectation maximization (EM) framework.
• Identifying the first two moments (FTM) of the perturbation (UI) analytically.

Main Results:

• The proposed SMCCF significantly enhances orbit estimation performance by utilizing identified UI-FTM.
• SMCCF simultaneously corrects both the state estimation and its covariance.
• Simulations demonstrate superior performance of SMCCF compared to existing NESs and standard identification algorithms.

Conclusions:

• The novel SMCCF offers a more effective approach to orbit state estimation by incorporating both mean and covariance information of unknown inputs.
• This identification-based filtering design overcomes the computational burden of INPI in traditional NESs.
• The method shows significant potential for improving the accuracy and reliability of space target orbit determination.