Optimized multi-point hemispherical grid model with adaptive grid division based on the prior information of multipath error

Abstract

The multi-point hemispherical grid model (MHGM) utilizes residual of double-differenced observations to extract precise multipath error information. It models the entire network of multipath error effects across different stations to achieve effective error correction. However, because all the parameters are estimated collectively using the least squares method, the increased number of grid point parameters can significantly consume memory, CPU, and other computing resources required for modeling. In response to the computational resource consumption challenge associated with fixed-resolution MHGM in multi-station applications, a space domain adaptive grid division method is proposed to optimize the modeling of multipath errors. This approach utilizes prior distribution information of multipath errors to optimize the grid structure. It reduces the number of grids in areas where multipath errors exhibit minimal changes, and provides detailed parameterization for areas with significant variations. Experimental results demonstrate the effectiveness of this method in significantly reducing the number of estimated parameters using MHGM. In statistical analysis of double-differenced phase observation residuals with fixed ambiguities, as the number of estimated parameters in the MHGM decreases to only 24.6 % of the fixed-resolution approach, memory usage during parameter estimation remains a mere 6 % of that required in the fixed-resolution approach. This highlights its potential value in mitigating multipath errors when modeling GNSS large-scale network data.