精密单点定位不等精度观测值的RKF研究

发布时间：2018-05-19 12:43

本文选题：抗差卡尔曼滤波 + 精密单点定位　；参考：《武汉大学学报(信息科学版)》2017年03期

【摘要】：推导了精密单点定位含有粗差观测数据的M-LS滤波原理,对等价权阵采用三段降权函数实现抗差。从新息和残差的协方差关系出发,利用对粗差敏感的残差标准差作为抗差因子。通过迭代减弱卫星间载波残差及其抗差因子的相关性。针对载波和伪距观测值不等观测精度和不相关性,采用双抗差因子实现静态抗差卡尔曼滤波(robust Kalman filtering,RKF)。采用标准卡尔曼滤波、基于新息RKF、基于残差的增益矩阵双抗差因子RKF、基于残差的等价权阵双抗差因子RKF等4种模型,分别对一组实测数据解算分析。结果表明,基于新息RKF对精度较高的载波粗差不敏感;基于残差的增益矩阵RKF对载波较小的粗差抗差效果较差,且发生粗差历元时刻的状态参数与真值偏差较大;而基于残差构造的等价权阵双抗差因子RKF可以非常精确和高效地实现抗差,单个卫星粗差对测站位置参数影响小于1mm。
[Abstract]:This paper derives the M-LS filtering principle of precision single point positioning with gross error observation data. The equal valence array uses three segment weight reduction functions to achieve tolerance. Starting from the covariance relationship between the new interest and the residual, the residual standard deviation sensitive to the coarse difference is used as the tolerance factor. The correlation between the carrier residual difference and the difference of the difference factor is weakened by iteration. The carrier and pseudo range observation values are unequal and uncorrelated, and the dual anti difference factor is used to realize static robust Calman filter (robust Kalman filtering, RKF). The standard Calman filter, based on the new interest RKF, the gain matrix double anti difference factor RKF based on the residual, 4 models based on the residual equivalent weight matrix double anti difference factor RKF, are divided. Do not analyze a set of measured data. The results show that the RKF based on the new interest is insensitive to the high precision carrier difference, and the gain matrix RKF based on the residual error has a bad effect on the smaller carrier difference, and the difference between the state parameters and the true value of the gross error is larger than that of the true value, and the equivalent weight matrix based on the residual structure is RKF. It is very precise and efficient to achieve robust, and the influence of gross errors of single satellite on station location parameters is less than 1mm.
【作者单位】：中国矿业大学国土环境与灾害监测国家测绘地理信息局重点实验室;中国矿业大学江苏省资源环境信息工程重点实验室;
【基金】：国家自然科学基金(41074010) 高等学校博士学科点专项科研基金(20130095110022) 江苏高校优势学科建设工程项目(SZBF2011-6-B35)~~
【分类号】：P228

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