Robust Parallel Bayes Estimation for Large-scale GNSS Baseline Vector Network

Based on the characteristics of large-scale GNSS baseline vector network adjustment and the IGGⅢ scheme, an improved double-factor equivalent weight with different weight-dropping efficiency schemes were derived. Methods and adaptability of parallel programming are compared, and a calculation procedure based on multi-task classification and processing is proposed and it achieves parallel estimation. Data from IGS are used in an experiment, and the results show that the proposed method not only takes full advantage of the prior information of coordinates and effectively restrains the influence of the baseline vector outliers, but also makes full use of the hardware platform, then significantly improves the computational efficiency.