自适应蒙特卡洛法评定全站仪测距不确定度

全站仪测距精度的校准需要在标准基线场上进行，由于野外环境不可控和气象条件波动剧烈，因此判断全站仪的测量结果的可靠程度具有重要意义。为了解决全站仪测距不确定度评定模型的非线性和输入量强相关等问题，本文首先采用了自适应蒙特卡洛法进行不确定度评定，然后与GUM的不确定度评定结果进行对比，当测距距离为1 176 m时，自适应蒙特卡洛法评定的不确定度结果为2.2 mm，GUM为2.6 mm，结果显示两种不确定度评定方法的测量结果均在合理预期之内，且自适应蒙特卡洛法评定的不确定度置信区间更窄。自适应蒙特卡洛法结合了大量数据样本和自适应优化仿真次数的优势，不仅对全站仪测距过程中的各项误差源引入的不确定度分量评估更为全面，而且在保证了全站仪测距不确定度评定结果准确的同时，相比于蒙特卡洛法节约了70%的样本数量。

Evaluation of uncertainty of distance measurement by total station using adaptive Monte Carlo method

The calibration of total station distance measurement accuracy needs to be carried out on a standard baseline field, and it is of great significance to judge the reliability of the measurement results of the total station due to the uncontrollable field environment and the drastic fluctuation of meteorological conditions. In order to solve the problems of nonlinearity and strong correlation of inputs of the total station distance measurement uncertainty evaluation model, this paper firstly adopts the adaptive Monte Carlo method to evaluate the uncertainty, and then compares the uncertainty evaluation results with those of the GUM. When the ranging distance is 1 176 m, the uncertainty evaluation results of the adaptive Monte Carlo method is 2.2 mm, and the GUM is 2.6 mm. The results show that the measurement results of both uncertainty assessment methods are within reasonable expectations, and the uncertainty confidence interval of the adaptive Monte Carlo method is narrower. The adaptive Monte Carlo method combines the advantages of a large number of data samples and adaptive optimization of the simulation times, which not only provides a more comprehensive assessment of the uncertainty components introduced by various error sources in the process of total station distance measurement, but also saves 70% of samples compared with the Monte Carlo method, while guaranteeing the accuracy of the uncertainty assessment results of the total station distance measurement.