函数模型和随机模型双约束的GNSS数据融合及其性质

GNSS Data Fusion with Functional and Stochastic ModelConstraints as well as Property Analysis

  • 摘要: 目的 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。

     

    Abstract: Objective The data fusion for GNSS processing often have some functional model constraints amongparameters or some stochastic model(prior information for total or part parameters)constraints.Inthis paper,the parameter estimators for dual functional and stochastic information constraints arepresented in least squares principle,and the posteriori precision estimators are also derived.As somespecial examples,the parameter estimators with only functional model constraints or stochastic modelconstraints 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 errorin the functional model constraints will result in compulsive twist in estimated parameters which iscalled“hard twist”.The errors of the stochastic model constraints will also result in bias of parameterestimates,which is called“soft bias”.An actual GPS network with measurements of two epochs,2011and 2012,are employed in the data fusion,by which the contribution and effects of the function-al and stochastic model constraints are analyzed.

     

/

返回文章
返回