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Sensors 2016, 16(7), 1073; doi:10.3390/s16071073

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter

Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
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Academic Editor: Vittorio M. N. Passaro
Received: 18 April 2016 / Revised: 7 July 2016 / Accepted: 7 July 2016 / Published: 12 July 2016
(This article belongs to the Section Physical Sensors)
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Abstract

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved. View Full-Text
Keywords: fiber optic gyroscope (FOG); auto regressive (AR) model; Sage-Husa adaptive Kalman filter (SHAKF); online model; random drift fiber optic gyroscope (FOG); auto regressive (AR) model; Sage-Husa adaptive Kalman filter (SHAKF); online model; random drift
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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

Sun, J.; Xu, X.; Liu, Y.; Zhang, T.; Li, Y. FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter. Sensors 2016, 16, 1073.

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