Mikio Bando

GNSS fault detection with unmodeled error

Graphical Abstract Abstract This paper provides a method for ‘fault detection’ on a GNSS/INS positioning system with unmodeled errors. Many GNSS/INS positioning systems apply testing with Maharanobis distance for fault detection of GNSS position. However, this method requires an accurate system model. If there are some inaccuracies in the model, we struggle to adjust the best parameters on the system model by trial and error. This fact sometimes reduces extendability of the system. Thus, we advance an unmodeled error discerning method focused on non-stationarity in residuals between observation time-series and estimated values. Our proposed method provides a stable period for observation time series. In that period, we can correctly calculate the covariance of unmodeled error and also the predisposition of unmodeled error to displace the origin of Mahalanobis distance to an appropriate point. This new procedure gives a better distribution for testing for fault detection than the conventional procedure. We verify our proposed fault detection in real-world measurement with a test vehicle on an irregular ground test circuit. The results confirmed that our proposed method improved the accuracy of localization. The proposed method will help us to apply positioning systems easily to other vehicles.