Influence of Different Combination Methods of GPS Elevation Fitting
1 College of Geomatics and Geoinformation, Guilin University of Technology,12 Jiangan Road, Guilin 541004, China
2 Guangxi Key Laboratory of Spatial Information and Geomatics, 12 Jiangan Road, Guilin 541004, China
Abstract Based on the characteristic and applicable scope of plane fitting, quadratic surface fitting and GA-BP neural networkmodels, nonlinear and linear combined methods are proposed to integrateadvantages of each model and improve the accuracy and reliability of height fitting. We consider an RBF neural networkcombination, a weighted least squares support vector machine (WLSSVM) portfolio, an optimal weighted combination and an optimal non-negative variable weight combination. The consequences of these different combined methodson GPS height fitting precisionare compared and analyzed. The results showthat different combination methods generate different accuracy of GPS Height Fitting. The WLSSVM and optimal non-negative variable weight combinations are superior to the others: they have stronger reliability, can better control the residual extremes, effectively shorten the error range, and havehigher conversion accuracy.
