An enhanced outlier processing approach based on the resilient mathematical model compensation in GNSS precise positioning and navigation

Abstract

The abnormal measurements are widely existent in Global Navigation Satellite System (GNSS) precise positioning and navigation mainly because of the diffraction, reflection, refraction, and even non-line-of-sight reception. However, when multiple outliers exist in GNSS measurements, traditional methods including test procedure or robust estimation usually cannot work well. This study proposed an enhanced outlier processing approach based on the resilient mathematical model compensation. Specifically, first, to avoid excessive deletion, the total number of measurements is considered in the adaptive test procedure with the help of a scale factor. Second, in adaptive robust estimation, the total number of remaining measurements is also considered, thus making it more compatible with the adaptive test procedure. In addition, to overcome the potential inappropriate reweighting operator, different shrinking factors are adopted for code and phase measurements according to their precision, respectively. To verify the effectiveness of the proposed method, one static monitoring experiment and one kinematic vehicle experiment were conducted, where the method without outlier processing, traditional test procedure, traditional robust estimation, and the proposed method were all used. For the static experiment, the ambiguity resolution and positioning solutions of the proposed method perform best. The positioning accuracy of the float and fixed solutions can be improved by approximately 67.4% and 77.6% on average under challenging environments, respectively. For the kinematic experiment, the performance is also the best in terms of positioning availability and accuracy by using the proposed method.