Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

Abstract

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.

Automatic Kalman-filter-based wavelet shrinkage denoising of 1D stellar spectra

“…We are also exploring the impact of developing and using a custom mother wavelet instead of the 'Haar' wavelet. Another possible line of research would be to apply Kalman filtering to reconstructed decomposition levels, estimating the noise and process covariances via an ARMA process (Huang 2015). One might also investigate deviations from the assumption of Gaussianity for the process or measurement noise, or of the assumption of zero covariance between them (Liu et al 2016).…”

Modeling of Inertial Rate Sensor Errors Using Autoregressive and Moving Average (ARMA) Models

“…In literature, several time series models have been widely used in many fields such as industry, science and engineering. Among the other model, auto regressive (AR) and moving average (MA) models have been most popular and since then widely used for forecasting [14][15][16]. The combination of AR and MA models has been used for inertial sensors error modeling.…”

“…Allan variance (AV) is a popular time domain method has been widely used for identifying and quantifying random errors in the presence of inertial sensor [14]. Cluster based analysis is used to develop the AV technique.…”

“…In the recent years, MEMS devices have been developed and tested successfully for low-end accuracy applications [12,13]. MEMS sensor operates for a long time under poor condition and it generates the noise due to internal circuits and electronics interferences of the MEMS sensor [14][15][16]. As a result, noise and drift are generated at the MEMS output.…”

“…The modified Yule-Walker method is used estimate the model parameters. Once an optimal ARMA model is defined, a suitable adaptive Kalman filter can be applied to minimize the drift of inertial sensors [14,30].…”