A robust adaptive indirect in-motion coarse alignment method for GPS/SINS integrated navigation system

Strap-down inertial navigation system (SINS) is an autonomous sensor that can be used for car-mount in-motion coarse alignment. To overcome the shortcoming of high computational burden for the existing in-motion coarse alignment method, we propose a novel fast indirect in-motion coarse alignment method. In the proposed method, the measurement model is constructed by using the output of a global positioning system (GPS) and a SINS, and the rotation vector is used as the attitude parameterization to establish the process model. Moreover, considering that existing linear state estimators cannot always yield a satisfactory performance when the distribution of the measurement noise deviates from the assumed Gaussian distribution and inaccurate noise covariance matrix exists in the filtering model. We propose a novel adaptive Student's t-based Kalman filter (STKF) to overcome these challenges. Car-mounted simulation and experiments show the correctness of the proposed in-motion alignment method and adaptive STKF.

Automated summary

A novel fast indirect in-motion coarse alignment method is proposed, utilizing the output of GPS and SINS to construct the measurement model and using the rotation vector as the attitude parameterization. An adaptive Student's t-based Kalman filter is introduced to handle challenges of measurement noise distribution deviation and inaccurate noise covariance matrix.