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Sensors 2015, 15(10), 25277-25286; doi:10.3390/s151025277

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

Automation Department, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, China
Academic Editor: Vittorio M.N. Passaro
Received: 3 August 2015 / Revised: 31 August 2015 / Accepted: 2 September 2015 / Published: 30 September 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [951 KB, uploaded 30 September 2015]   |  

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. View Full-Text
Keywords: random noise modeling; robust Kalman filtering; ARMA modeling random noise modeling; robust Kalman filtering; ARMA modeling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, L. Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter. Sensors 2015, 15, 25277-25286.

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