An Effective Method for GPS GDOP Clustering using Ant Colony Optimization Algorithm

Satellite geometry, which represents the geometry locations of the GPS satellites as seen by the receiver(s), plays a very important role in the total positioning accuracy. The stronger the satellite geometry strength is, the higher the positioning accuracy obtained will be. As such, the overall positioning accuracy of GPS is measured by the combined effect of the unmodeled measurement errors and the effect of the satellite geometry. This paper presents satellites geometry clustering for good navigation satellites subset selection. This approach is based on clustering of the satellites Geometry Dilution of Precision (GDOP) factor using Ant Colony Optimization (ACO) algorithm which is used newly in solving data-clustering problems and developed from imitating the technique of real ants finding the shortest way from their nests and food source. The proposed method use pheromone to evaluate individual colony’s iterative result. Without matrix inversion required, the ACO-based approach is capable of evaluating all subsets of satellites and hence reduces the computational burden. Simulation results show this method is more efficient to converge upon the optimal value in the GPS GDOP clustering.