TY - GEN
T1 - Influence of Parameters in Kalman-filter-based Method on Image Quality for Electrical Capacitance Tomography
AU - Wang, Ying
AU - Xu, Lijun
AU - Sun, Shijie
AU - Lu, Xupeng
AU - Sun, Jiangtao
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/17
Y1 - 2021/5/17
N2 - As a powerful tool to get a recursive solution of least squares estimation, the Kalman filter has been used for image reconstruction in Electrical Capacitance Tomography (ECT). In the Kalman-filter-based image reconstruction method, some key parameters, e.g., initial guess, observation noise covariance and initial estimate error covariance, greatly influence the performance of the method. Inappropriate values of these parameters may cause a series of problems, such as lower convergence rate, artifacts, or filter divergence. This paper aims to analyze the influence of the parameters on the image quality for ECT and guide the selection of the parameters. Numerical simulation and experiment were carried out and the results show that with an initial guess obtained by linear back projection (LBP) method and a good match of observation noise covariance and initial estimate error covariance, the performance of the Kalman-filter-based method can be improved.
AB - As a powerful tool to get a recursive solution of least squares estimation, the Kalman filter has been used for image reconstruction in Electrical Capacitance Tomography (ECT). In the Kalman-filter-based image reconstruction method, some key parameters, e.g., initial guess, observation noise covariance and initial estimate error covariance, greatly influence the performance of the method. Inappropriate values of these parameters may cause a series of problems, such as lower convergence rate, artifacts, or filter divergence. This paper aims to analyze the influence of the parameters on the image quality for ECT and guide the selection of the parameters. Numerical simulation and experiment were carried out and the results show that with an initial guess obtained by linear back projection (LBP) method and a good match of observation noise covariance and initial estimate error covariance, the performance of the Kalman-filter-based method can be improved.
KW - Electrical Capacitance Tomography
KW - Kalman filter
KW - image reconstruction
KW - inverse problem
KW - parameter selection
UR - https://www.scopus.com/pages/publications/85113708227
U2 - 10.1109/I2MTC50364.2021.9459913
DO - 10.1109/I2MTC50364.2021.9459913
M3 - 会议稿件
AN - SCOPUS:85113708227
T3 - Conference Record - IEEE Instrumentation and Measurement Technology Conference
BT - I2MTC 2021 - IEEE International Instrumentation and Measurement Technology Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2021
Y2 - 17 May 2021 through 20 May 2021
ER -