We present a method for PPP adjustment based on Recursive Least Square Estimation (RLSE). This method estimates the stochastic model of GPS, GLONASS, Galileo and BeiDou observations by using RLS-Variance Components Estimation (RLS-VCE). The accuracy and the convergence time of horizontal, altitudinal and three-dimensional components is compared by using both nominal and RLS-VCE method so that the estimated accuracy for these coordinate components in suggested method is more accurate than nominal method. The accuracy of horizontal, altitudinal and three-dimensional components in this method are 1.3, 1.25 and 1.8 cm respectively in comparison to nominal method with 4, 3.8 and 5.6 cm. By considering 15 cm as the convergence condition, convergence time is estimated 3 and 5.3 min for suggested and nominal methods respectively. The obtained RMSE after convergence time in proposed and nominal methods are 7.1 and 9.2 cm respectively. According to the presented results, when Multi-GNSS observations are combined, the lowest RMSE are estimated for the coordinate components. As time passes, the stochastic model becomes more stable, therefore, the presented method will be more accurate.

我们提出了一种基于递归最小二乘估计（RLSE）的PPP调整方法。该方法通过使用RLS-方差分量估计（RLS-VCE）估计GPS，GLONASS，伽利略和北斗观测站的随机模型。同时使用标称方法和RLS-VCE方法比较了水平，垂直和三维分量的精度和收敛时间，因此建议方法中这些坐标分量的估计精度比标称方法更准确。与标称方法4、3.8和5.6 cm相比，此方法中水平，垂直和三维分量的精度分别为1.3、1.25和1.8 cm。通过将15 cm作为收敛条件，建议方法和名义方法的收敛时间分别估计为3分钟和5.3分钟。在建议的和名义的方法中，收敛时间后获得的RMSE分别为7.1 cm和9.2 cm。根据给出的结果，当组合多个GNSS观测值时，将为坐标分量估计最低的RMSE。随着时间的流逝，随机模型变得更加稳定，因此，所提出的方法将更加准确。