An improved adaptive Kalman filter for denoising fiber optic gyro drift signal

In this paper, an innovation based adaptive estimation Kalman filter (IAE-AKF) with double transitive factors is proposed for denoising the fiber optic gyroscope (FOG) signal. In this algorithm, double transitive adaptive factors are described in two stages. The transitive factor is introduced into the predicted state vector equation in stage one, where as in second stage, adaptive factor is scaled with measurement noise covariance matrix (R). These adaptive factors are developed based on the innovation sequence in adaptive Kalman filter. The predicted state error and measurement noise covariance matrix are updated by the double transitive adaptive factor in the process of iteration in stage one and two respectively. This algorithms is applied for denoising FOG signal in both static and dynamic conditions. The performance of proposed algorithm is compared with Conventional Kalman filter (CKF) and AKF with transitive factor. The precision improvement of FOG is calculated by variance and standard deviation, the predicted results revealed that the proposed algorithm is an efficient algorithm in drift denoising of FOG signal. In dynamic condition, the mean squared error (MSE) and root MSE (RMSE) values are calculated before and after denoising of FOG signal using proposed algorithm.