Abstract: Considering the flight environment and motion characteristics of aerospace vehicle, the noise statistical characteristics of the navigation sensors’ errors can’t be completely known, which may seriously degrade the filtering precision or even cause filtering divergence if the conventional Kalman Filter is used. An online calibration method for IMU errors of SINS based on the innovation-based adaptive filtering is proposed, which can overcome the weakness of that the conventional Kalman filter should know the statistical characteristics of the system noise and measurement noise in advance. An error calibration model of 27-D high-order state variables is established, including the installation error, scale factor error and random constant error of IMU, and an online calibration algorithm based on the innovation-based adaptive filtering is proposed, which can adjust the covariance matrices of system noise and measurement noise dynamically. Simulation shows that the proposed method performs higher calibration accuracy and better navigation performance compared to the conventional Kalman filter and Salychev O adaptive filtering algorithm. It is proved by the field test that this method could calibrate the IMU residual errors effectively, improve the navigation accuracy and bring a great convenience for engineering application.

Key words: control science and technology, inertial navigation system, inertial measurement unit error, online calibration, adaptive filtering