Fiber optic gyroscope threshold denoising based on EMD-LWT

Dai Shaowu; Zheng Baidong; Dai Hongde; Nie Zijian

doi:10.12086/oee.2019.180333

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Dai Shaowu, Zheng Baidong, Dai Hongde, et al. Fiber optic gyroscope threshold denoising based on EMD-LWT[J]. Opto-Electronic Engineering, 2019, 46(5): 180333. doi: 10.12086/oee.2019.180333

Citation:

Dai Shaowu, Zheng Baidong, Dai Hongde, et al. Fiber optic gyroscope threshold denoising based on EMD-LWT[J]. Opto-Electronic Engineering, 2019, 46(5): 180333. doi: 10.12086/oee.2019.180333

Fiber optic gyroscope threshold denoising based on EMD-LWT

Coastal Defense Force Naval Aviation University, Yantai, Shandong 264001, China

Fund Project: Supported by Defense Science and Technology Project Foundation of China (F062102009)

More Information

Corresponding author: Zheng Baidong, E-mail: 2219229392@qq.com

Received Date 21 June 2018

Revised Date 25 October 2018

Published Date 01 May 2019

Abstract

Abstract

Fiber optic gyroscope (FOG) drift data is often submerged in various noises backgrounds. It is very difficult to compensate for modeling drift signals directly. In order to better eliminate the noise mixed in the FOG temperature drift data, a hybrid EMD-LWT filtering algorithm based on empirical mode decomposition (EMD) and lifting wavelet transform (LWT) threshold denoising was proposed for gyro signals preprocessing. Firstly, the noise signal of fiber optic gyro is decomposed by EMD, and the noise term and the mixed modal term of the intrinsic mode functions (IMF) are judged according to the information entropy. Then the noise term is de-noised by LWT and the mixed modal term is denoised by wavelet transform (WT). A static test was performed on an interferential FOG to verify the effectiveness of the algorithm and compared with WT and LWT. The experimental results show that the proposed EMD-LWT filtering algorithm has better filtering effect. After processing, the root mean square error (RMSE) of the reconstructed signal is reduced by 63%, which effectively removes the noise in the FOG output.
- fiber optic gyroscope /
- wavelet analysis /
- EMD-LWT /
- filtering

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References

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Overview

Overview

Overview: Fiber optic gyro (FOG) is an inertial sensor based on the Sagnac effect. It has the advantages of high reliability, high measurement accuracy, and ease of integration. It has become an ideal device for inertial navigation systems. The collected FOG drift data is affected by many factors such as the light source, fiber bending, and ambient temperature, making it often submerged in the noise and leading to difficulties in direct modeling compensation. In order to establish an accurate error compensation model, data preprocessing is demanded to output data on the gyro.
In this paper, a hybrid EMD-LWT filtering algorithm based on empirical mode decomposition (EMD) and lifting wavelet transform (LWT) threshold denoising is proposed to preprocess gyro signals. Firstly, the steps of empirical mode decomposition are introduced. After the signal is decomposed by EMD, a finite number of high-to-low frequency intrinsic mode functions (IMFs) are obtained. The low order part represents the high frequency part of the signal, which usually contains a sharp part or noise; An IMF with a large order corresponds to the low-frequency part of the signal, and it is generally considered that the noise in the low-frequency component has little effect. It is decomposed into noise-dominated IMF sets, where noise and effective information coexist and a signal low-frequency trend. The threshold filtering method based on EMD is a process to select and threshold three types of IMF sets. The information entropy and the energy of the signal serve as a measurement of the complexity of the signal and determine the boundaries of the noise component and the mixed modal component.
Considering that the traditional EMD time-scale filtering algorithm simply removes one or more IMF components to achieve filtering, resulting in the useful signals along the corresponding components being deleted together. It will lead to severe signal distortion. The lifting wavelet analysis is introduced into the EMD method, and the high-frequency IMF component is subjected to the narrowband re-decomposition of the lifting wavelet to improve the resolution of the high-frequency component; considering the noise decomposition after being distributed on each IMF component, combined with the characteristics of wavelet threshold denoising. All IMF components are subjected to wavelet threshold denoising.
A static FOG data was collected as a test signal for verifying the effectiveness of the algorithm. The hybrid EMD-LWT was compared with the wavelet transform (WT) and the lifting wavelet transform (LWT) threshold filtering methods. The simulation results show that the root mean squared error (RMSE) of the signal is reduced by 63% through the EMD-LWT filtering algorithm and the denoising effect is obvious.