The provision of accurate ionospheric corrections in PPP-RTK enormously improves the performance of single-receiver user integer ambiguity resolution (IAR), thus enabling fast high precision positioning. While an external provider can disseminate such corrections to the user with a time delay, it is the task of the user to accurately time-predict the corrections so that they become applicable to the user positioning time. Accurate time prediction of the corrections requires a dynamic model in which the process noise of the corrections has to be correctly specified. In this contribution, we present an estimation method to determine the process noise variance of PPP-RTK corrections using single-receiver GNSS data. Our focus is on variance estimation of the first-order slant ionospheric delays, which allows one to analyze how the ionospheric process noise changes as a function of the solar activity, receiver local time, and receiver geographic latitude. By analyzing 11-year GNSS datasets, it is illustrated that estimates of the ionospheric process noise are strongly correlated with the solar flux index F10.7. These estimates also indicate a seasonal variation, with the highest level of variation observed during the spring and autumn equinoxes.

PPP-RTK 中提供精确的电离层校正极大地提高了单接收器用户整数模糊度分辨率 (IAR) 的性能，从而实现快速高精度定位。虽然外部提供者可以以一定的时间延迟向用户传播此类校正，但用户的任务是准确地时间预测校正，以便它们适用于用户定位时间。校正的准确时间预测需要一个动态模型，其中必须正确指定校正的过程噪声。在此贡献中，我们提出了一种估计方法，用于使用单接收器 GNSS 数据确定 PPP-RTK 校正的过程噪声方差。我们的重点是一阶倾斜电离层延迟的方差估计，这使得人们能够分析电离层过程噪声如何随着太阳活动、接收器本地时间和接收器地理纬度的函数而变化。通过对11年GNSS数据集的分析表明，电离层过程噪声的估计与太阳通量指数F10.7密切相关。这些估计还表明季节性变化，在春分和秋分期间观察到的变化水平最高。