Classification of GNSS SNR data for different environments and satellite orbital information

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

Analysis of GPS reflected signals based on SNR measurements:land versus water

Abstract The transmitted Global Positioning System (GPS) signal has Right Hand Circular Polarization (RHCP) and it changes to Left Hand Circular Polarization (LHCP) after being reflected. The proportions of RHCP and LHCP power levels depend on characteristics of reflecting surface and satellite elevation angle. The change of polarization can be evaluated by comparing the measured RHCP and LHCP levels. This paper reports the results of Signal-to-Noise Ratio (SNR) data for direct and reflected GPS signal components measured over sea and land surfaces. First, field measurements with two dual polarized antennas having both RHCP and LHCP are performed in both environments. Then, SNR-based analysis is done to compare reflection levels between two reflecting surfaces. The results show that the SNR of reflected signal from seawater is on average 2 dB or more higher than that of signal reflected from asphalt or ground

3-D ray tracing based GPU accelerated field prediction radio channel simulator

Abstract A ray tracing simulator for urban and indoor environments is introduced. The simulator uses NVIDIA graphics processing unit (GPU) accelerated CUDA parallel computing platform and programming mode and the OptiX Ray Tracing Engine. As a use case, channel characteristics for Global Navigation Satellite System (GNSS) satellites are simulated and compared with measurements in an urban area. The speedup achieved by parallel processing allows computation of multiple relevant reflections characteristic of a satellite channel