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Extended Integrative Combined Orbit Determination Models Based on Prior Trajectory Information and Optimal Weighting Algorithm

机译:基于先验轨迹信息和最优加权算法的扩展组合组合轨道确定模型

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For multi-LEO combined orbit determination (COD) satellite-network based on space-based tracking telemetry and command (STTC) satellites, kinematic orbit information can be obtained only using the method of precise point positioning (PPP) based on observation models, but the results are not very precise because of observation data precision and GDOP of constellation. If kinematic orbit information of low earth orbit (LEO) can be taken full advantage of participating in dynamic precise orbit determination (POD) and making them achieve best matching, integrative COD models based on kinematic and dynamic information can be constructed to realize dynamic smoothness of kinematic information. Firstly, single LEO difference positioning model and the corresponding algorithm was designed, and integrative COD models were constituted based on kinematic and dynamic trajectory information. Then extended integra-tive COD models based on prior trajectory information considering nonlinear semi-parametric modeling of observation models errors and sparse parameters modeling of dynamic models were established, and the optimal weighting algorithm of multi-structural nonlinear COD models was designed. Theoretical analysis and simulation computation results show that COD weighting method based on prior trajectory information can realize LEOs dynamic information optimal matching with kinematic prior information, and can restrain nonlinear influence factor including measure models and ephemeris errors to the effects of POD precision by considering models errors modeling and dynamic models sparse parameters denotation.
机译:对于基于空基跟踪遥测和指挥(STTC)卫星的多LEO组合轨道确定(COD)卫星网络,只能使用基于观测模型的精确点定位(PPP)方法获得运动轨道信息,但是由于观测数据的精度和星座图的GDOP,结果不是很精确。如果可以充分利用低地球轨道(LEO)的运动轨道信息参与动态精确轨道确定(POD)并使其达到最佳匹配,则可以构建基于运动和动态信息的集成COD模型以实现轨道的动态平滑性。运动学信息。首先,设计了单一的LEO差分定位模型和相应的算法,并基于运动和动态轨迹信息构建了集成的COD模型。然后建立了基于先验轨迹信息的扩展综合COD模型,该模型考虑了观测模型误差的非线性半参数建模和动力学模型的稀疏参数建模,并设计了多结构非线性COD模型的最优加权算法。理论分析和仿真计算结果表明,基于先验轨迹信息的COD加权方法可以实现LEOs动态信息与运动学先验信息的最佳匹配,并且可以通过考虑模型误差来抑制包括测量模型和星历误差在内的非线性影响因素对POD精度的影响。建模和动态模型稀疏参数表示。

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