New Method for Transforming Global Positioning System Height into Normal Height Based on Neural Network

Abstract

In China, normal height, which is the height above the geoid calculated using the mean normal gravity along the plumb line, is used in engineering applications. However, the adjusted global positioning system (GPS) height is the height above the surface of the WGS-84 ellipsoid. Thus, it is necessary to convert a GPS height into a normal height. Normally, the conicoid fitting method (CFM) and the neural network method (NNM) are used for this purpose in China, but each has its own advantages and disadvantages. After studying these two methods, a new method (CF&NNM) is conceived. The structure of the back-propagation neural network and detailed algorithm of the CFM, NNM, and CF&NNM are discussed. The procedure of the new method is introduced. A practical engineering example is used to study these three different methods. The results by the three methods are listed. It is demonstrated that the CF&NNM could produce better results than either the CFM or the NNM in deriving the normal height from the GPS height. The theory of the CF&NNM is analyzed.