A fast systematic calibration method based on Sage Husa adaptive filter

DOI:	10.11809/bqzbgcxb2023.03.032
Keywords:	systematic calibration; Kalman filter; Suga Husa adaptive filter; filtering model; error parameter
Abstract:	In order to improve the filter convergence speed of the systematic calibration algorithm of strapdown inertial navigation system (SINS), this paper proposes a fast systematic calibration method for Sage Husa adaptive filter. The noise parameters of the Kalman filter are analyzed to carry out the adaptive identification of the measurement noise variance matrix. A six position static calibration scheme is used to reduce the dimension of the filter model by designing intermediate parameters on the premise of satisfying observability of the filter. The simplified model after dimension reduction can be better applied to Sage Husa adaptive filter. The experimental results show that, compared with the conventional Kalman filter systematic calibration method, both filter methods can calibrate all inertial measurement unit (IMU) error parameters. The relative errors of calibration are all less than 0.6%. However, the calibration time shortens from 5 min to 6 s, which improves the speed of SINS systematic calibration.
Published:	2023-03-28
PDF HTML