Enhancing multi-GNSS precise point positioning performance of low-cost receivers by implementing a MAD-based quality control procedure

Outlier detection is an important prerequisite for achieving accurate Precise Point Positioning (PPP) solutions. Without a suitable procedure for identifying outliers, unexpected effects such as divergence or degradation may arise during parameter estimation. To ensure reliable positioning, this study proposes a new robust quality control strategy based on the median absolute deviation (MAD) for detecting and handling outliers, employing predicted residuals (innovations) as input. In the proposed method, innovation vectors of code and phase observations are evaluated separately. Following the detection and identification steps, outlier code observations are removed from the filter update, whereas phase outliers are handled by reinitializing their ambiguity parameters through variance inflation in the predicted covariance matrix. Moreover, an existing quality control algorithm incorporating both posterior and standardized residuals was adapted and tested as the second method. The robust adaptive Kalman filter with the IGG (Institute of Geodesy and Geophysics) III function was then applied for a performance comparison with the two proposed methods. In this regard, GNSS observations collected during static and kinematic experimental tests conducted with a low-cost GNSS receiver were processed using offline real-time products of the Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA). The positioning accuracies for the static tests demonstrated that the IGG-III robust adaptive Kalman filter is inferior to the presented strategies and exhibits substantial solution deviations, especially when there is limited visibility of the satellites. While multi-GNSS combinations significantly improved the performance of this method, they did not reach the sub-decimeter 3D RMS delivered by our proposed techniques. Additionally, the kinematic experiment showed that the two methods produced comparable results and were superior to the IGG-III. Indeed, these approaches achieved a 3D accuracy of about 12 cm, whereas the IGG-III method yielded an accuracy of 17.7 cm. As a result of static open-sky environment tests and a kinematic suburban experiment, the MAD-based method, which is straightforward to implement, achieved strong performance in detecting outliers and contributed to the promising potential of low-cost receivers. The findings demonstrate that the proposed method can support practical applications, such as vehicle navigation, precision agriculture, and precise positioning of UAVs/drones.