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Journal of Sensors
Volume 2015 (2015), Article ID 564041, 8 pages
http://dx.doi.org/10.1155/2015/564041
Research Article

Dynamic Allan Variance Analysis Method with Time-Variant Window Length Based on Fuzzy Control

1Jiangsu Key Laboratory of Internet of Things and Control Technologies, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China
2Navigation Research Center, College of Automation, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China
3Department of Civil and Environmental Engineering, Centre for Transport Studies, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

Received 9 March 2015; Accepted 19 May 2015

Academic Editor: Geoffrey A. Cranch

Copyright © 2015 Shanshan Gu et al. 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.

Abstract

To solve the problem that dynamic Allan variance (DAVAR) with fixed length of window cannot meet the identification accuracy requirement of fiber optic gyro (FOG) signal over all time domains, a dynamic Allan variance analysis method with time-variant window length based on fuzzy control is proposed. According to the characteristic of FOG signal, a fuzzy controller with the inputs of the first and second derivatives of FOG signal is designed to estimate the window length of the DAVAR. Then the Allan variances of the signals during the time-variant window are simulated to obtain the DAVAR of the FOG signal to describe the dynamic characteristic of the time-varying FOG signal. Additionally, a performance evaluation index of the algorithm based on radar chart is proposed. Experiment results show that, compared with different fixed window lengths DAVAR methods, the change of FOG signal with time can be identified effectively and the evaluation index of performance can be enhanced by 30% at least by the DAVAR method with time-variant window length based on fuzzy control.