An improved adaptive unscented Kalman filter for denoising the FOG signal

In strap down inertial navigation system (SINS), an interferometric fiber optic gyroscope (IFOG) is sensitive device to measure the rotation rate of an object. The IFOG output sustains with noise and random drift errors, which are influenced by the uncertainties of the external environment (like temperature, pressure, vibration) and sensor itself. Random drift is the main error source and it degrades the IFOG accuracy. To improve the precision of IFOG and to suppress these noises, the drift modeling and noise compensation methods are required. In this paper, a residual based adaptive unscented Kalman filter (AUKF) is proposed for denoising the IFOG signal. In this algorithm, window average method is used for estimating the measurement noise covariance matrix (R̂) based on covariance matching technique. The proposed algorithm is utilized for denoising IFOG test signal under static and dynamic environment. Allan variance analysis is used to analyze and quantify the noise sources of IFOG sensor. Based on the suggested technique, the angle random walk (N) and bias instability (Bs) values are reduced by an order of 10 times compared with actual value. The performance improvement of proposed algorithm in maneuvering condition is indicated by the reduced root mean square error values (RMSE). The performance of the proposed algorithm is compared with Unscented Kalman filter (UKF) algorithm. Simulation result reveals that the proposed algorithm is a valid solution for drift denoising the IFOG signal.