The iinfluence of different global tropospheric models on baseline precision in a local GPS network: case of the Malaysian Johor RTKnet

Abstract

GPS network is restricted by the effects of de-correlate atmospheric refraction on the GPS signal. The two main components of the atmospheric refractions are the ionospheric and tropospheric refraction, showing distinct spatial correlation characteristic in the vertical direction. Although the ionospheric bias can be mitigated using dual frequency receivers, tropospheric bias is currently one of the major error sources in GPS Network, which limits the full functionality of GPS Positioning. The delay of the radio signals caused by the troposphere can range from 2m at zenith to 20m at lower elevation angles (below 10 degrees). In order to reduce the tropospheric effects, global tropospheric models derived experimentally using radiosonde data are been employed today. These models are derived using data obtained from Europe and America. Considering the location of Malaysia in the equatorial and tropical region, it is susceptible to high tropospheric effect thereby having an adverse effect on the GPS signals which in- turns affect positioning. With the establishment of the Malaysian RTK GPS Network (MyRTKnet) as one of the latest innovation of a realtime precision positioning in meeting up with the nation’s development, security and defence; the need to investigate the impact of the different global tropospheric models became imperative. This paper provides details on the network test carried out by comparing GPS baseline results obtained from three different global tropospheric models which include Saastamoinen model, Hopfield model and the Neil model by applying standard and assumed local surface meteorological data. The results show that, there are no statistically significant differences in the performance of the three tropospheric models. Similarly, the result indicates a statistically significant correlation in the application of the standard and local surface meteorological data. On the overall, the Saastamoinen model produced a more precise baseline result with 89% in the horizontal component and 92% in the height component than the Hopfield and Neil models.