The Research of GPS Elevation Fitting Considering the Influence of Covariance Function

Abstract

The common method to determine Quasi-Geoids is GPS leveling however the Quasi-Geoid of this method determined is a kind of trend surface which not take the physical property of geoid into consideration, and the fitting method is surface fitting which only consider the surveying error, lead to inaccurate fitting result. In allusion to these problems, Remove-restore method is used to remove the long wave information of earth gravity field model to get more smooth residual gravity height anomaly, then compared the influence of different covariance function to the fitting result of least square collocation which take surveying error and model error into account. The results show that Gaussian and resemble Gaussian function can achieve higher fitting precision to the large area with height anomaly value changes significance; the Remove-restore method can effectively improve the fitting precision to least square collocation method which depend on the covariance value of each points.

Research on GPS Height Fitting Based on Abc Optimized LSSVM

“…Therefore, how to obtain high-precision elevation anomaly values is a problem to be solved in GPS measurement work. At present, intelligent algorithms such as particle swarm optimization, genetic algorithm and artificial neural network are often applied to the construction of GPS height fitting model, and the accuracy of the elevation anomaly value to be sought is further improved (Liu, L. L.,2014). Liu, et.al (2013) has proposed to use the genetic algorithm to optimize the RBF neural network to achieve the purpose of global search for the optimal radial basis function center value, so that the fitted model can better predict the elevation.…”