Application of adaptive Unscented Kalman Filter for angular velocity calculation in GFSINS

In order to improve the angular velocity calculation precision in Gyro-free Strapdown Inertial Navigation System (GFSINS), an angular velocity calculation method based on adaptive Unscented Kalman Filter (UKF) was proposed. A general angular velocity calculation model with time-varying process noise was established, which was not limited to a certain kind of accelerometer configuration. Combining Sage-Husa suboptimal maximum a posteriori (MAP) noise estimator with UKF algorithm, both the first moment and the second moment of the process noise could be real-timely estimated in the precondition of known measurement noise. The filter was kept from divergence through guaranteeing the half positive definitiveness of the process noise's covariance matrix. Based on a kind of nine-accelerometer configuration, the proposed algorithm was simulated and also contrasted with the traditional integration method and evolution method. The simulation results indicated that the adaptive UKF algorithm was better than the integration method and evolution method, which could effectively improve the angular velocity calculation precision and avoid the problems of error accumulation, sign misjudgment and gross error data production.