基于多源观测的联合定轨数据融合建模及最优加权算法

Combined Orbit Determination Data Fusion Modeling and Optimal Weighting Algorithm Based on Multi-source Measurement Data

针对基于双星定位系统的近地卫星联合定轨中的多源观测数据的融合处理问题,分别建立了基于测量噪声独立同分布和测量噪声相关的多源融合测量模型,提出了一种基于矩阵Cholesky分解的广义测量模型和测量噪声去相关方法。设计了多源融合测量模型的多结构非线性最优加权参数估计实现算法,并以双星定位系统的星敏感器测角与距离和测量信息为例,进行了联合定轨仿真实验。理论分析与仿真计算结果表明,相对于仅用距离和测量信息与平均加权方式,基于多源观测数据的最优加权联合定轨方法能够进一步改善卫星定轨精度。

Aiming at multi-source measure data fusion process of low earth orbit （LEO） combined orbit determination （COD） based on biatellite positioning system （BPS）, two sorts of multi-source data fusion measure models based on independent same distribution and correlation of measure noises were constructed, and a method based on matrix Cholesky decomposition and noises de-correlation was put forward. A multi-structural nonlinear optimal weighting parameters estimation algorithm of multi-source data fusion measure models was designed, and simulation experiments taking LEO COD of BPS star sensor angle information and range sum data for example were carried out. Theoretical analyses and simulation computation results show that the algorithm of optimal weighting parameters estimation can ameliorate satellites COD precision ulteriorly contrasting against that of range sum information and average weighting mode.