Abstract: Aiming at solving the problem that the accuracy and stability of Kalman filtering (KF) in a position and orientation system (POS) will be affected if there are outliers in global positioning system (GPS) outputs, this article proposes a method of KF restraining outliers based on orthogonality of innovation in the data processing of POS. This method detectes whether there are outliers in the position and velocity data of GPS by judging if the orthogonality of innovation is lost or not, and assigns an activation function as the weight to each element of measurement, which can keep the orthogonal properties of the innovation sequence and the outliers can be detected and corrected. Field-test results show that this method is effectively resistant to outliers in GPS data, and can obtain improvement of accuracy in position and velocity from ten to hundred times compared with conventional KF.

Key words: position and orientation system, SINS/GPS integration, outlier, Kalman filtering, innovation, orthogonality