Abstract
Fiber Optic Gyroscope (FOG) is a key component in Inertial Navigation System. The performance of FOG degrades due to different types of random errors in the measured signal. Although Kalman filter and its variants like Sage-Husa Kalman filters are being used to denoise the Gyroscope signal the performance of Kalman filter is limited by the initial values of measurement and process noise covariance matrix, and transition matrix. To address this problem, this paper uses modified Sage-Husa adaptive Kalman filter to denoise the FOG signal. In this work, the random error of fiber optic gyroscope is modeled using a first order auto regressive (AR) model and used the coefficients of the model to initialize the transition matrix of Sage-Husa Adaptive Kalman filter. Allan variance analysis is used to quantify the random errors of the measured and denoised signal. The performance of proposed algorithm is compared with conventional Kalman filter and the simulation results show that the modified SageHusa adaptive Kalman filter (SHAKF) algorithm outperforms the conventional Kalman filter technique while denoising FOG signal.
Published Version
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