Crowdsourced GNSS Satellite SNR in Degraded Environments for Dependability Improvement

Abstract

Global Navigation Satellite Systems (GNSS) localization uses time of arrival (TOA) to measure the distance between satellite and rover antenna. The key for TOA technique is the signal propagation should be straight geometrically. However, when the train runs in harsh environments, the GNSS signal quality can be degraded due to obstacles. GNSS signals are usually blocked by buildings and other obstacles, resulting in reflection and reflection, which leads to multipath positioning errors of LOS and NLOS. In the case of LOS multipath error, the GNSS receiver tracks the direct and reflected / diffracted composite signal, and the reflected / diffracted signal affects the correlation function tracking loop of the direct signal in GNSS. In the case of NLOS multipath error, GNSS receiver directly tracks reflected or diffracted signal because there is no direct satellite signal. In both cases, the multipath signal will have a large noise error range which is reflected in the change of SNR. In the GNSS solution, various GNSS receivers are used to obtain the SNR of each satellite to achieve the fully coverage of the satellite signal reception quality. Thus with limited GNSS receiver observation results, of the crowdsourcing of NLOS signal inspection will deliver the observation results in a more convenient way. In the open environment scenario, the relationship between elevation angle and SNR can be fitted by a linear function. However, in GNSS signal reception degraded environments (urban canyon etc.), The SNR at the elevation angle of the occlusion boundary will be drastically reduced. This will give us the boundary of the occlusion range. Based on this principle, we can map the environmental characteristics of different sections of the railway. The railway environment can generally be divided into open scenario, road cutting scenario, half sky scenario, urban canyon scenario and tunnel scenario. 5 typical scenario all have distinct environmental characteristics. By matching the segment with the typical environmental characteristics, the scenario situation of the segment is determined, and then the accuracy of satellite positioning is given.