Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter
In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain...
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doaj-aeb146f1741f4a508d188a8046fa7b472020-11-25T02:23:05ZengMDPI AGSensors1424-82202020-03-01201897189710.3390/s20071897Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman FilterYi Yang0Fei Li1Yi Gao2Yanhui Mao3School of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaSchool of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaSchool of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaSchool of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaIn the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory.https://www.mdpi.com/1424-8220/20/7/1897multi-sensor combined measurementquaternionunscented kalman filtersquare root filteradaptive fading factor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Yang Fei Li Yi Gao Yanhui Mao |
spellingShingle |
Yi Yang Fei Li Yi Gao Yanhui Mao Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter Sensors multi-sensor combined measurement quaternion unscented kalman filter square root filter adaptive fading factor |
author_facet |
Yi Yang Fei Li Yi Gao Yanhui Mao |
author_sort |
Yi Yang |
title |
Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter |
title_short |
Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter |
title_full |
Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter |
title_fullStr |
Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter |
title_full_unstemmed |
Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter |
title_sort |
multi-sensor combined measurement while drilling based on the improved adaptive fading square root unscented kalman filter |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-03-01 |
description |
In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory. |
topic |
multi-sensor combined measurement quaternion unscented kalman filter square root filter adaptive fading factor |
url |
https://www.mdpi.com/1424-8220/20/7/1897 |
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