Lattice-based clustering and genetic programming for coordinate transformation in GPS applications

Coordinate transformation is essential in many georeferencing applications. Level-wised transformation can be considered as a regression problem and done by machine-learning approaches. However, inaccurate and biased results are usually derived when training data do not uniformly distribute. In this paper, the performance of regression-based coordinate transformation for GPS applications is discussed. A lattice-based clustering method is developed and integrated with genetic programming for building better regression models of coordinate transformation. The GPS application area is first partitioned into lattices with lattice sizes being determined by the geographic locations and distribution of the GPS reference points. Clustering is then performed on lattices, not on data points. Each cluster of lattices serves as a training data set for a genetic regression model of coordinate transformation. In this manner, the data points contained in the different lattices can be considered to be of the same importance. Biased regression results caused by the imbalanced distribution of data can also be eliminated. The experimental results show that the proposed method can further improve the positioning accuracy than previous methods.