大规模GNSS基线向量网抗差并行贝叶斯估计

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

针对大规模GNSS(global navigation satellite system)基线向量网平差的特点,在IGGⅢ方案的基础上,基于不同降权效率的双因子等价权的改进模型,比较了并行编程方法及环境的适应性,提出了基于多任务划分与处理的并行计算流程,实现了相关观测抗差贝叶斯估计的并行计算。实验采用IGS全网的数据,结果表明,相关抗差并行贝叶斯估计不仅能够充分利用坐标参数的先验信息,有效抵制基线向量异常误差的影响,而且能够充分利用已有的硬件平台,显著提高抗差计算效率。

Based on the characteristics of large-scale GNSS baseline vector network adjustment and the IGGⅢscheme,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.