Journal
MEASUREMENT
Volume 172, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2020.108834
Keywords
In-motion coarse alignment; Integrated navigation system; Kalman filter; Adaptive Student's t-based Kalman filter; Outliers
Funding
- Outstanding Innovation Scholarship for Doctoral Candidate of CUMT [2019YCBS029]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
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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.
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.
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