Improved Measurement Accuracy of a Laser Interferometer: Extended Kalman Filter Approach

2010 ◽  
Vol E93-A (10) ◽  
pp. 1820-1823 ◽  
Author(s):  
Wooram LEE ◽  
Dongkyun KIM ◽  
Kwanho YOU
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Accuracy ◽  
Laser Interferometer ◽  
Filter Approach

Detection of Seismic Wave with Laser Interferometer

2014 ◽  
Vol 681 ◽  
pp. 66-69
Author(s):  
Woo Ram Lee ◽  
Eun Hwan Oh ◽  
Min Woo Lim ◽  
Kwan Ho You
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Seismic Wave ◽  
Measurement Accuracy ◽  
Laser Interferometer ◽  
Nonlinearity Error ◽  
Detection Process ◽  
Wave Detection ◽  
Wave Measurement ◽  
Heterodyne Laser Interferometer

In this paper, we propose a seismic wave detection process using a laser interferometer as a seismometer. The laser interferometer system is an important equipment with its remarkable accurate capability for displacement measurement. During the process of the seismic wave measurement, however, environmental and nonlinearity error are occurred in a heterodyne laser interferometer. Through the extended Kalman filter compensation, the distortion is reduced. With a recursive STA/LTA algorithm, the PS-time of the seismic wave is determined and then the epicenter distance can be derived. Through some simulations, it is demonstrated that the proposed algorithm can reduce error factors and improve the measurement accuracy of a seismometer.

scholarly journals Vibration Error Correction for the FOGs-Based Measurement in a Drilling System Using an Extended Kalman Filter

2021 ◽  
Vol 11 (14) ◽  
pp. 6514
Author(s):  
Lu Wang ◽  
Yuanbiao Hu ◽  
Tao Wang ◽  
Baolin Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Accuracy ◽  
Original Data ◽  
Calibration Method ◽  
Error Model ◽  
Nonlinear Error ◽  
Measurement While Drilling ◽  
Drilling System ◽  
Vibration Error

Fiber-optic gyroscopes (FOGs)-based Measurement While Drilling system (MWD) is a newly developed instrument to survey the borehole trajectory continuously and in real time. However, because of the strong vibration while drilling, the measurement accuracy of FOG-based MWD deteriorates. It is urgent to improve the measurement accuracy while drilling. Therefore, this paper proposes an innovative scheme for the vibration error of the FOG-based MWD. Firstly, the nonlinear error models for the FOGs and ACCs are established. Secondly, a 36-order Extended Kalman Filter (EKF) combined with a calibration method based on 24-position is designed to identify the coefficients in the error model. Moreover, in order to obtain a higher accurate error model, an iterative calibration method has been suggested to suppress calibration residuals. Finally, vibration experiments simulating the drilling vibration in the laboratory is implemented. Compared to the original data, compensated the linear error items, the error of 3D borehole trajectory can only be reduced by a ratio from 10% to 34%. While compensating for the nonlinear error items of the FOG-based MWD, the error of 3D borehole trajectory can be reduced by a ratio from 44.13% to 97.22%. In conclusion, compensation of the nonlinear error of FOG-based MWD could improve the trajectory survey accuracy under vibration.

scholarly journals A Phase Difference Measurement Method Based on the Extended Kalman Filter for Coriolis Mass Flowmeters

2019 ◽  
Vol 19 (2) ◽  
pp. 48-52
Author(s):  
Nan Chen ◽  
Shangchun Fan ◽  
Dezhi Zheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Phase Difference ◽  
Measurement Method ◽  
Measurement Accuracy ◽  
Single Phase ◽  
Signal Frequency ◽  
Difference Measurement ◽  
Mallat Algorithm ◽  
Interference Signals

Abstract According to the characteristics of stable single-phase flow, a phase difference measurement method based on the extended Kalman filter is proposed in this paper for use with Coriolis mass flowmeters. Firstly, the Mallat algorithm is applied to filter out interference signals. Then, the frequency and phase difference of the two reconstructed signals are detected through the extended Kalman filter. Compared with the sliding Goertzel algorithm or discrete time Fourier transform, the proposed method does not need to predict the signal frequency and avoids quadratic error. Simulations and experiments show that the proposed method has stronger anti-interference, higher measurement accuracy and lower relative error than the existing method based on the Hilbert transformation.

Experimentally Validated Extended Kalman Filter Approach for Geomagnetically Induced Currents Measurement

2021 ◽  
pp. 1-1
Author(s):  
Behzad Behdani ◽  
Mohsen Tajdinian ◽  
Mehdi Allahbakhshi ◽  
Marjan Popov ◽  
Miadreza Shafie-khah ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Geomagnetically Induced Currents ◽  
Induced Currents ◽  
Filter Approach

scholarly journals Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

2019 ◽  
Vol 39 (4) ◽  
pp. 835-849 ◽  
Author(s):  
Jinshan Huang ◽  
Xianzhi Li ◽  
Xiongjun Yang ◽  
Zhupeng Zheng ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Extended Kalman Filter ◽  
Experimental Validation ◽  
Structural Parameters ◽  
Wide Band ◽  
Unknown Inputs ◽  
Filter Methods ◽  
Filter Approach ◽  
Shear Building

The extended Kalman filter is a useful tool in the research of structural health monitoring and vibration control. However, the traditional extended Kalman filter approach is only applicable when the information of external inputs to structures is available. In recent years, some improved extended Kalman filter methods applied with unknown inputs have been proposed. The authors have proposed an extended Kalman filter with unknown inputs based on data fusion of partially measured displacement and acceleration responses. Compared with previous approaches, the drifts in the estimated structural displacements and unknown external inputs can be avoided. The feasibility of proposed extended Kalman filter with unknown inputs has been demonstrated by some numerical simulation examples. However, experimental validation of the proposed extended Kalman filter with unknown inputs has not been conducted. In this paper, an experiment is conducted to validate the effectiveness of the proposed approach. A five-story shear building model subjected to an unknown external excitation of wide-band white noise is conducted. Moreover, the data fusion of partially measured strain and acceleration responses from the building is adopted as it is difficult to accurately measure structural displacement in practice. Identified results show that the recently proposed extended Kalman filter with unknown inputs can be applied to identify structural parameters, structural states, and the unknown inputs in real time.

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