Notice of RetractionA Hybrid Conversion of GPS Height Approach Based on Neural Networks and EGM96 Gravity Model

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

How to transfer GPS heights into normal heights has become a hotspot in survey engineering. With the development of computer and information technology, artificial neural networks (ANN) has been used widely, which has the character of the high parallel distributed processing, associative memory abilities, self-organization, self-learning and strong nonlinear mapping abilities, and the theory has proved that one ANN, having deviation and one S-type hidden layer or more plus one linear output layer, could approach any rational function, and the ANN has been used in GPS heights conversion. EGM96 is a gravity model of the earth, which has high precision of long wavelength contribution, so the paper puts forward a hybrid approach for conversion of GPS height Based on neural networks and EGM96. Remove the long wavelength from GPS/leveling points, next construct a model from the leaving parts fitted by BP neural network to calculate other GPS points, and restore the long wavelength to the points. The experiments show that the hybrid approach is much validated and something useful is obtained.