Least-Squares Variance Component Estimation Applied to Stochastic Model Refinement of GNSS Difference Positioning
Abstract The authors apply least-squares variance component estimation to evaluate unknown parameters of five different elevation-dependent stochastic models with double differenced GPS observables, and then use negative variance components as indicators to detect the applicability of models. Afterwards, the tangent, cosine,exponential function and the identical weight models are compared by calculating the baseline components. Test results indicate that the positioning accuracy and efficiency are dependent on elevation, and that one should choose the realistic stochastic model.
Key words :
GNSS
stochastic model
least-squares variance component estimation (LS-VCE)
elevation model
Cite this article:
YANG Ting,CHEN Yijin,CHEN Haonan. Least-Squares Variance Component Estimation Applied to Stochastic Model Refinement of GNSS Difference Positioning
[J]. jgg, 2017, 37(2): 196-199.
YANG Ting,CHEN Yijin,CHEN Haonan. Least-Squares Variance Component Estimation Applied to Stochastic Model Refinement of GNSS Difference Positioning
[J]. jgg, 2017, 37(2): 196-199.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2017/V37/I2/196
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