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Sensors 2015, 15(2), 2496-2524; doi:10.3390/s150202496

Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

1
Department of Automation, Harbin Engineering University, Harbin 150000, China
2
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150000, China
3
Department of Information and Communication Engineering, Harbin Engineering University, Harbin 150000, China
*
Author to whom correspondence should be addressed.
Received: 13 June 2014 / Accepted: 2 December 2014 / Published: 23 January 2015
(This article belongs to the Special Issue Optical Gyroscopes and Navigation Systems)

Abstract

As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor. View Full-Text
Keywords: Allan variance; stochastic errors; online estimation methods; nonlinear state-space model; neural-extended Kalman filter Allan variance; stochastic errors; online estimation methods; nonlinear state-space model; neural-extended Kalman filter
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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MDPI and ACS Style

Miao, Z.; Shen, F.; Xu, D.; He, K.; Tian, C. Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter. Sensors 2015, 15, 2496-2524.

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