The standard deviation of the nonlinear functional value can be obtained by the law of covariance propagation. Existing covariance propagation methods of the nonlinear model contain the following problems: the approximate function method requires complicated derivative operation; the Monte Carlo method has a high simulated burden and low convergence effectiveness. To overcome these disadvantages, we introduce the Quasi-Monte Carlo (QMC) method and design the implementation process of the QMC method for covariance propagation with independent or correlated observations. Considering that the QMC method cannot balance the number of simulations and the accuracy of the results, a novel QMC algorithm for small numbers of batches simulation is proposed, namely, Adaptive Quasi-Monte Carlo (AQMC). The QMC method and the AQMC algorithm are applied in the forward intersection and covariance propagation of the GNSS baseline vector in geodetic measurement. The results verify the effectiveness of the QMC method and the AQMC algorithm. Compared with the adaptive Monte Carlo method, the AQMC method can improve the computational efficiency by almost 84.4%. The proposed approach provides a new idea for the covariance propagation of the nonlinear model.

非线性函数值的标准偏差可以通过协方差传播定律得到。现有的非线性模型协方差传播方法存在以下问题：近似函数法需要复杂的导数运算；Monte Carlo 方法具有较高的模拟负担和较低的收敛效率。为了克服这些缺点，我们引入了准蒙特卡罗 (QMC) 方法，并设计了 QMC 方法的实现过程，用于具有独立或相关观测值的协方差传播。考虑到QMC方法不能平衡模拟次数和结果的准确性，提出了一种新的用于小批量模拟的QMC算法，即自适应准蒙特卡罗（AQMC）。QMC方法和AQMC算法应用于大地测量中GNSS基线向量的前向交集和协方差传播。结果验证了QMC方法和AQMC算法的有效性。与自适应蒙特卡罗方法相比，AQMC 方法可以将计算效率提高近 84.4%。所提出的方法为非线性模型的协方差传播提供了新的思路。