Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography Based on An Improved Kalman Filter Model

Y Wu; Y Jiang; H Ji; B Wang; Manuchehr Soleimani

doi:10.1109/TIM.2025.3545218

Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography Based on An Improved Kalman Filter Model

Y Wu, Y Jiang, H Ji, B Wang, Manuchehr Soleimani

Zhejiang University

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Abstract

By developing an improved Kalman filter (KF) model, a new imaging method of capacitively coupled electrical resistance tomography (CCERT) is proposed for monitoring multiphase flows. In the improved KF model, the innovative idea of state evolution learning is proposed to learn the spatiotemporal correlations between the fluid states, and the level-based state representation is integrated to reduce the state dimension and improve the computation efficiency. Correspondingly, the imaging method based on the improved KF model is proposed for CCERT. Experimental results of the gas-liquid two-phase flow verify the effectiveness and potential of the proposed method. The improved KF model can quickly and effectively learn and utilize the flow distribution regularities across time frames, and can achieve better imaging performance in both quality and efficiency. The largest average RIE and the smallest average CC of the images obtained by the proposed method are 0.125 and 0.831 for single-object distributions, respectively.

Original language	English
Journal	IEEE Transactions on Instrumentation and Measurement
Early online date	24 Feb 2025
DOIs	https://doi.org/10.1109/TIM.2025.3545218
Publication status	E-pub ahead of print - 24 Feb 2025

Funding

National Natural Science Foundation of China (Grant Number: 62201502) Natural Science Foundation of Zhejiang Province (Grant Number: LQ22F030001) State Key Laboratory of Industrial Control Technology (Zhejiang University) (Grant Number: ICT2023A09)

Funders	Funder number
Zhejiang Association of Automation and Cangnan Association for Science and Technology
State Key Laboratory of Industrial Control Technology
National Natural Science Foundation of China	62201502
Natural Science Foundation of Zhejiang Province	LQ22F030001
Zhejiang University	ICT2023A09

Keywords

Electrical resistance tomography
Kalman filter
capacitively coupled electrical resistance tomography
image reconstruction
state evolution learning

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering

Access to Document

10.1109/TIM.2025.3545218

Image Reconstruction (accepted author manuscript)Accepted author manuscript, 1.9 MBLicence: CC BY

Cite this

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title = "Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography Based on An Improved Kalman Filter Model",

abstract = "By developing an improved Kalman filter (KF) model, a new imaging method of capacitively coupled electrical resistance tomography (CCERT) is proposed for monitoring multiphase flows. In the improved KF model, the innovative idea of state evolution learning is proposed to learn the spatiotemporal correlations between the fluid states, and the level-based state representation is integrated to reduce the state dimension and improve the computation efficiency. Correspondingly, the imaging method based on the improved KF model is proposed for CCERT. Experimental results of the gas-liquid two-phase flow verify the effectiveness and potential of the proposed method. The improved KF model can quickly and effectively learn and utilize the flow distribution regularities across time frames, and can achieve better imaging performance in both quality and efficiency. The largest average RIE and the smallest average CC of the images obtained by the proposed method are 0.125 and 0.831 for single-object distributions, respectively.",

keywords = "Electrical resistance tomography, Kalman filter, capacitively coupled electrical resistance tomography, image reconstruction, state evolution learning",

author = "Y Wu and Y Jiang and H Ji and B Wang and Manuchehr Soleimani",

year = "2025",

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day = "24",

doi = "10.1109/TIM.2025.3545218",

language = "English",

journal = "IEEE Transactions on Instrumentation and Measurement",

issn = "0018-9456",

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TY - JOUR

T1 - Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography Based on An Improved Kalman Filter Model

AU - Wu, Y

AU - Jiang, Y

AU - Ji, H

AU - Wang, B

AU - Soleimani, Manuchehr

PY - 2025/2/24

Y1 - 2025/2/24

N2 - By developing an improved Kalman filter (KF) model, a new imaging method of capacitively coupled electrical resistance tomography (CCERT) is proposed for monitoring multiphase flows. In the improved KF model, the innovative idea of state evolution learning is proposed to learn the spatiotemporal correlations between the fluid states, and the level-based state representation is integrated to reduce the state dimension and improve the computation efficiency. Correspondingly, the imaging method based on the improved KF model is proposed for CCERT. Experimental results of the gas-liquid two-phase flow verify the effectiveness and potential of the proposed method. The improved KF model can quickly and effectively learn and utilize the flow distribution regularities across time frames, and can achieve better imaging performance in both quality and efficiency. The largest average RIE and the smallest average CC of the images obtained by the proposed method are 0.125 and 0.831 for single-object distributions, respectively.

AB - By developing an improved Kalman filter (KF) model, a new imaging method of capacitively coupled electrical resistance tomography (CCERT) is proposed for monitoring multiphase flows. In the improved KF model, the innovative idea of state evolution learning is proposed to learn the spatiotemporal correlations between the fluid states, and the level-based state representation is integrated to reduce the state dimension and improve the computation efficiency. Correspondingly, the imaging method based on the improved KF model is proposed for CCERT. Experimental results of the gas-liquid two-phase flow verify the effectiveness and potential of the proposed method. The improved KF model can quickly and effectively learn and utilize the flow distribution regularities across time frames, and can achieve better imaging performance in both quality and efficiency. The largest average RIE and the smallest average CC of the images obtained by the proposed method are 0.125 and 0.831 for single-object distributions, respectively.

KW - Electrical resistance tomography

KW - Kalman filter

KW - capacitively coupled electrical resistance tomography

KW - image reconstruction

KW - state evolution learning

UR - https://www.scopus.com/pages/publications/85219151619

U2 - 10.1109/TIM.2025.3545218

DO - 10.1109/TIM.2025.3545218

M3 - Article

SN - 0018-9456

JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

ER -

Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography Based on An Improved Kalman Filter Model

Abstract

Funding

Keywords

ASJC Scopus subject areas

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