Online Stochastic Modelling for Network-Based GPS Real-Time Kinematic Positioning

Abstract

Baseline length-dependent errors in GPS RTK positioning, such as orbit uncertainty, and atmospheric effects, constrain the applicable baseline length between reference and mobile user receiver to perhaps 10-15km. This constraint has led to the development of network-based RTK techniques to model such distance-dependent errors. Although these errors can be effectively mitigated by network-based techniques, the residual errors, attributed to imperfect network functional models, in practice, affect the positioning performance. Since it is too difficult for the functional model to define and/or handle the residual errors, an alternative approach that can be used is to account for these errors (and observation noise) within the stochastic model. In this study, an online stochastic modelling technique for network-based GPS RTK positioning is introduced to adaptively estimate the stochastic model in real time. The basis of the method is to utilise the residuals of the previous segment results in order to estimate the stochastic model at the current epoch. Experimental test results indicate that the proposed stochastic modelling technique improves the performance of the least squares estimation and ambiguity resolution.