Dynamically Adjusting Filter Gain Method for Suppressing GNSS Observation Outliers in Integrated Navigation

Abstract

Global Navigation Satellite Systems (GNSSs) are easily influenced by the external environment. Signals may be lost or become abnormal thereby causing outliers. The filter gain of the standard Kalman filter of a loosely coupled GNSS/inertial navigation system cannot change with the outliers of the GNSS, causing large deviations in the filtering results. In this paper, a method based on a χ2-test and a dynamically adjusting filter gain method are proposed to detect and separately to suppress GNSS observation outliers in integrated navigation. An indicator of an innovation vector is constructed, and a χ2-test is performed for this indicator. If it fails the test, the corresponding observation value is considered as an outlier. A scale factor is constructed according to this outlier, which is then used to lower the filter gain dynamically to decrease the influence of outliers. The simulation results demonstrate that the observation outlier processing method does not affect the normal values under normal circumstances; it can also discriminate between single and continuous outliers without errors or omissions. The impact time of outliers is greatly reduced, and the system performance is improved by more than 90%. Experimental results indicate that the proposed methods are effective in suppressing GNSS observation outliers in integrated navigation.