Abstract

In this paper, a data classification method for analyzing the aspects of Signal-to-Noise Ratio (SNR) for Global Navigation Satellite System (GNSS) in real conditions is introduced. Different parts of measured environments and the orbital information of satellites are used as criteria for data classification. It consists of: 1) taking fish eye images of measured routes; 2) dividing measured environments into four potential sub environments (open area, forest area, single building blockage, and street canyon); 3) classifying satellites into nine different groups as function of elevation angles; and 4) creating a table containing the information of mean and standard deviation of SNR for different environments and satellite elevation angles. Results show good correlation of SNR’s between same sub environments for different satellite elevation ranges which offer useful insight to regenerate a generalized set of SNR parameters in the laboratory environment for the development of 3D GNSS channel model.