Application of Robust Mean-Difference Filtering to Relative Navigation of Formation Spacecraft

Abstract

This paper firstly introduces the basic knowledge required for relative navigation of formation spacecraft, which involves the description of the main coordinate systems, the modelling method of relative orbit dynamics of formation spacecraft, the introduction of common interstellar measurement models and the description of the measurement model of the measurement sensors, and then it is divided into two subsections to study the relative navigation problem of the formation spacecraft in two cases. Aiming at the problems of low filtering accuracy and slow convergence speed of the traditional extended Kalman filtering algorithm for formation spacecraft relative navigation, and considering the linear GPS measurement model, this paper simplifies and applies the standard mean difference filtering algorithm to spacecraft formation relative navigation. Simulation results show that the Simplified Divide Difference Filter (SDDF) proposed in this paper is suitable for the design of formation spacecraft relative navigation filters. In view of the non-Gaussian characteristics of the GPS measurement system in spacecraft formation flight, the Huber-based Divided Difference Filter (H-SDDF) algorithm is used to improve the robustness of the above SDDF algorithm, and is applied to the design of relative navigation filters for spacecraft formation flight, and the simulation results verify that the H-SDDF algorithm proposed in this paper is suitable for the design of relative navigation filters for spacecraft formation flight. The simulation results verify the effectiveness of the H-SDDF algorithm proposed in this paper. In summary, this paper focuses on the robust mean difference filter algorithm. The research includes the design of relative navigation filter algorithm for formation spacecraft and the full nonlinear state estimation problem when both the filter system and the measurement model are nonlinear. The main problems solved are: improving the filter estimation accuracy and enhancing the robustness of the filtering process. Finally, theoretical analyses and numerical simulations verify that the research done in this paper has a certain degree of improvement compared with the previous ones.