Extended Integrative Combined Orbit Determination Models based on Sparse Parameters Representation and Combined Optimal Weighting Algorithm

摘要

For multi-satellite combined orbit determination (COD) network system based on GEO-IGSO-MEO satellite constellation and LEOs, the to-be-estimated satellites trajectory parameters results are not very precise because of satellites orbit perturbation indefinable physical models, observation models without adequately considering model error or measure mixed error, imprecise parameters estimation algorithm or linear least squares (LS) estimation algorithm, which cannot meet high precision application requirements of LEOs. Firstly, the high precision sparse parameters representation model of GEO-IGSO-MEO and LEOs orbit dynamics model is proposed. Above on this, the extended integrative COD models which combine parametric modeling and semi-parametric component representation based on measurement systematic errors and model errors are proposed. Secondly, the paper proposed weighting iterative wavelet estimation method of dynamics sparse parameters representation model, and LS approximation estimation method of non linear semi-parametric model is designed based on above models. Lastly, combined optimal weighting parameters estimation algorithm of extended integrative COD models which combines parametric estimation and non-parametric estimation are designed. Theoretic analysis and simulated computation results show that if only the physical model is used for COD, there is relatively big dynamical modeling error between dynamical model implied and the actual, which LEO precision cannot meet application requirements. When the high precision representation method of sparse parameters model and the optimized modeling method of observation model considering model errors can improve modeling and parameters estimation precision, and parameterized system error, model error and model parameters are estimated by extended integrative COD synchronously, so the orbit determination precision of LEOs can be improved evidently.