GNSS observations stochastic model influences all subsequent stages of data processing, from the possibility to reach the optimal parameters estimation, to the reliability and quality control of the solution. Nowadays, an uncontrolled use of GNSS stochastic models is common for both data processing and simulation missions, especially in commercial GNSS software packages. As a result, the variance–covariance matrices that are derived in the processing are inadequate and cause incorrect interpretations of the results. A proper method to evaluate the reliability of the stochastic model is needed to reflect the confidence level in statistic testing and simulation mission efforts. In this contribution, a novel method for evaluating the statistical nature of GNSS stochastic model is presented. The method relies on the deterministic nature of the integer ambiguity variable to examine and express the expected multinormal distribution of the double-difference adjustment results. The suggested method was used with a controlled experiment and 24 h of observations data to investigate how the statistical nature of the stochastic model is affected by different baseline lengths. The results indicate that as the baseline length increases, the stochastic model is less predictable and exposed to irregularities in the observation’s precision. Additionally, the reliability of the integer ambiguity resolution success rate (SR) was tested as part of the stochastic model evaluation. The results show a dramatic degradation in the SR prediction level when using an inadequate stochastic model, which suggests using extra caution when handling this parameter unless high-confidence reliable stochastic model is available.

GNSS观测随机模型会影响数据处理的所有后续阶段，从达到最佳参数估计的可能性到解决方案的可靠性和质量控制。如今，对于数据处理和模拟任务，尤其是在商用GNSS软件包中，GNSS随机模型的不受控制的使用已很普遍。结果，在处理中得出的方差-协方差矩阵不足，并导致对结果的错误解释。需要一种评估随机模型可靠性的适当方法，以反映统计测试和模拟任务工作中的置信度。在此贡献中，提出了一种评估GNSS随机模型的统计性质的新方法。该方法依靠整数模糊度变量的确定性来检查和表示双差调整结果的预期多正态分布。所建议的方法与对照实验和24小时的观察数据一起使用，以研究随机模型的统计性质如何受到不同基线长度的影响。结果表明，随着基线长度的增加，随机模型的可预测性降低，并且在观测精度上暴露于不规则性。此外，作为随机模型评估的一部分，对整数歧义分辨率成功率（SR）的可靠性进行了测试。结果表明，如果使用了不充分的随机模型，则SR预测水平将大大降低，