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《Acta Geodaetica et Cartographica Sinica》 2008-01
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GPS Baseline Solution Based on Empirical Mode Decomposition

WANG Jian, GAO Jing-xiang, WANG Jin-ling School of Environment and Spatial Informatics, China University of Mining and Technology (CUMT) ,Xuzhou 221008, China; School of Surveying and Spatial Information System, University of New South Wales(UNSW), Sydney 2052, Australia  
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.
【Fund】: 国家自然科学基金项目(40774010);; 国家教育部博士点基金项目(20040290503);; 中国矿业大学科技基金项目(2005B020);; 中国矿业大学青年科技基金项目(0P061016)
【CateGory Index】: P228.4
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