基于经验模态分解的GPS基线解算模型

非建模系统误差是影响高精度GPS基线解算精度的一个重要因素,文章给出基于经验模态分解的GPS基线解算模型,有效消弱系统误差对基线解的影响。在现有经验模态分解理论的基础上,定义经验模态分解的多尺度分解与重构结构,并由此给出基于经验模态的系统趋势分离模型,依据累积标准化模量的均值随尺度的变化确定系统误差与噪声分离尺度的选择标准。给出基于经验模态分解的GPS基线解算的技术路线,首先计算GPS相位双差观测方程的浮点解残差序列,分离出系统误差并用于修正GPS双差观测值,重新计算双差浮点解,采用Lambda算法固定整周模糊度,计算固定基线解,从而消弱系统误差对基线解算的影响,提高基线固定解的可靠性。并采用实测GPS数据验证模型,F-ratio指数与W-ratio指数表明系统误差消弱后,基线固定解可靠性得到明显提高,重新计算的残差序列表明系统误差得到很好的消弱。

GPS Baseline Solution Based on Empirical Mode Decomposition

The un-modeled systematic errors are the most important factors for high-precision GPS baseline solution. This paper presents a GPS baseline solution model based on the Empirical Mode Decomposition(EMD) with the advantage of eliminating the systematic errors effects. The EMD technique is a new signal processing method for nonlinear time series, which decomposes a time series into a finite and often small number of Intrinsic Mode Functions ( IMFs). The decomposition procedure is adaptive and data-driven which is suitable for non-linear data series analysis. A Multi-scale decomposition and reconstruction architecture is defined here on the basis of the EMD theory and the systematic errors mitigation model is demonstrated as well. A standard of the scale selection for the systematic errors elimination is given in terms of the mean of the accumulated standardized modes. Thereafter, the scheme of the GPS baseline solution based on the EMD is suggested. The float solution residuals of the double-difference(DD) observation equation are used to extract the systematic errors which are applied to modify the GPS DD measurements. Then the float solution is given again and the fixed solution is obtained by Lambda algorithm. The experimental results show that the proposed scheme dramatically improves the reliability of ambiguity resolution with the bigger /'-ratio and W-ratio indexes after systematic error elimination. Recalculation of residual series further demonstrates that the systematic errors have been eliminated at the same time.