New Neural Network-based Approaches for GPS GDOP Classification based on Neuro-Fuzzy Inference System, Radial Basis Function, and Improved Bee Algorithm

Abstract

Global positioning system (GPS) is the most widely used military and commercial positioning tool for real-time navigation and location. Geometric dilution of precision (GDOP) stands as a relevant measure of positioning accuracy and consequently, the performance quality of the GPS positioning algorithm. Since the calculation of GPS GDOP has a time and power burden that involves complicated transformation and inversion of measurement matrices, in this paper we propose hybrid intelligent methods, namely adaptive neuro-fuzzy inference system (ANFIS), improved ANFIS, and radial basis function (RBF), for GPS GDOP classification. Through investigation it is verified that the ANFIS is a high performance and valuable classifier. In the ANFIS training, the radius vector has very important role for its recognition accuracy. Therefore, in the optimization module, bee algorithm (BA) is proposed for finding the optimum vector of radius. In order to improve the performance of the proposed method, a new improvement for the BA is used. In addition, to enhance the accuracy of the method, principal component analysis (PCA) is utilized as a pre-processing step. Experimental results clearly indicate that the proposed intelligent methods have high classification accuracy rates comparing with conventional ones.