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.
Highlights
In strapdown inertial navigation systems (SINS), the ARMA model, which is one of the three time series models, is usually used to model or compensate for optic gyro random errors [1]
This paper developed a new ARMA modeling method for gyro random noise using a robust Kalman filtering
ARMA modeling method, we modeled the y-axis and z-axis fiber optic gyro (FOG) random noise using the developed method and Maximum Likelihood method, respectively
Summary
In strapdown inertial navigation systems (SINS), the ARMA model, which is one of the three time series models, is usually used to model or compensate for optic gyro random errors [1]. Kalman filter itself is seldom used to model the gyro random error anymore. The conventional ARMA modeling methods for gyro random noise, such as least square method, moment estimation method, and maximum likelihood method, all must first determine the model order before the ARMA model parameters are estimated. The order determination processes are complex, and the parameter estimations of the conventional methods require a large sample size and have a slow. Sensors 2015, 15 convergence speed [4,5,6,7] They cannot be applied to applications in which a fast and accurate. ARMA modeling method for gyro random noise is required. The developed modeling method does not require the complex model order determination. The order and the parameter estimates of the ARMA model can be identified simultaneously, quickly, and accurately by the developed method
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