FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter

Abstract

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

“…The time update of the iterative operation is described as shown in Equation (9). Combining the Sage-Husa filtering algorithm [58][59][60] with the UKF approach, the adaptive filtering method is introduced to correct the process noise variance, in which the measured noise variance in the formula real timely and the improved time-varying noise characteristics are applied to suit the nonlinearity. The adaptive filter can be used to obtain the measurement and updated data onto the iterative algorithm as shown in Equation (10).…”

Design and implementation of an adaptive Kalman filtering for the launcher of multiple launch rocket system

“…The root mean square error (RMSE) of the pitching angular speed of the launcher is selected as the evaluation. In addition, the UKF algorithm and the AKF in the work of Sun et al are implemented to be the comparisons, respectively.…”

“…In the work of Yuan and Lei, a novel algorithm, which is based on the modified Sage‐Husa adaptive Kalman filter, was proposed, where the predicted value of Sage‐Husa adaptive Kalman filter was adjusted in time by setting judgment and amendment rules. An alternative way was proposed to synchronously estimate Q and R by performing the process noise residual vector, the measurement redundancy index, and the variance‐covariance component estimation in the work of Wang et al A modified Sage‐Husa AKF was proposed and applied to the ship integrated navigation system to estimate the trajectory in the works of Sun et al and Liang et al In the work of Luo and Wang, an algorithm for tuning both the kinematic and measurement noise variance‐covariance matrices to produce a more robust and adaptive Kalman filter was suggested, where 2 stages, including robust estimation and adaptive estimation, were carried out to calculate the adaptive kinematic noise variance‐covariance tuning matrix. In the work of Zhang et al, the collaborative positioning algorithm based on AKF, according to the maximum‐likelihood criterion was put forward, where process noise covariance and observation noise covariance can be adaptively tuned to make the fusion filtering adapt to the changeable and complex noise environment.…”

Application of Fast Dynamic Allan Variance for the Characterization of FOGs-Based Measurement While Drilling

“…The fiber optic gyroscope (FOG), which has become mainstream in inertial navigation systems, is utilized to determine altitude for satellites and missiles [1]. The Ring Laser gyroscope is the ideal angular sensor for high-precision and long-endurance inertial navigation systems [2].…”