A Robust Stochastic Modelling Approach for Tight Integration of Precise Point Positioning and Ultra-Wide Band

Abstract

An accurate stochastic model is essential for achieving high-accuracy positioning solutions in the global navigation satellite system (GNSS) precise point positioning (PPP)/ultra-wide band (UWB) tightly coupled (TC) integration. Conventionally, a priori variances are used in the PPP/UWB TC integration to determine the weights of observations. However, a priori variances are difficult to obtain in complex environments since the stochastic characteristics of different observations depend heavily on environmental conditions. By contrast, the variance component estimation (VCE) method can provide a more accurate stochastic model by estimating the measurement uncertainties of different types of observations. Nevertheless, the VCE is susceptible to measurements' outliers and low redundancy in complex observation environments. To address these issues, a robust stochastic modelling approach for PPP/UWB TC integration is proposed by optimising the VCE with a robust estimation strategy and an adaptive moving window filter technique. Two kinematic experiments are conducted in signal-obstructed environments to validate the stochastic modelling approach. Results demonstrate that the three-dimensional (3D) positioning accuracy in the PPP/UWB TC integration is improved by over 47% after VCE optimisation. Compared to the a priori variance-based stochastic model, the robust stochastic modelling approach improves the 3D positioning accuracy by over 27%.