摘要
推导了基于函数模型和随机模型共同约束的参数最小二乘解及其验后精度估计模型;作为双约束参数解的特例,给出了仅含函数模型约束或仅含随机模型约束的参数解,以及无任何约束的参数解。侧重从理论上讨论了双约束参数解的性质,并分析指出,函数模型约束本身的误差将给参数估计带来强制性扭曲(简称"硬性影响"),先验随机模型本身的误差将给参数估计带来随机性影响(简称"软性影响")。最后,通过实际GNSS数据融合,分析了函数模型约束和随机模型约束的贡献。
The data fusion for GNSS processing often have some functional model constraints among parameters or some stochastic model （prior information for total or part parameters） constraints. In this paper, the parameter estimators for dual functional and stochastic information constraints are presented in least squares principle, and the posteriori precision estimators are also derived. As some special examples, the parameter estimators with only functional model constraints or stochastic model constraints are derived respectively. The properties of the data fusion with dual constraints are dis- cussed in theory. By analyzing the influences of the functional constraints, it is pointed that any error in the functional model constraints will result in compulsive twist in estimated parameters which is called ＂hard twist＂. The errors of the stochastic model constraints will also result in bias of parameter estimates, which is called ＂soft bias＂. An actual GPS network with measurements of two epochs, 2011 and 2012, are employed in the data fusion, by which the contribution and effects of the functional and stochastic model constraints are analyzed.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2014年第2期127-131,共5页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(41374019
41020144004)
国家863计划资助项目(2013AA122501-1)~~
