Volume 8, Issue 1
Fast Image Reconstruction Research Based on H∞ Filtering for Electrical Resistance Tomography

Qifang Liu, Han Yan & Xixiang Zhang

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 125-132.

Published online: 2015-08

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  • Abstract

In order to improve the image reconstructed quality affected by soft filed feature and the speed of dynamic on-line data processing in Electrical Resistance Tomography, we propose a fast image reconstruction algorithm based on H∞ filtering theory. Mainly, on the H∞ filtering principle, a dynamic system is formulated firstly, whose inputs have unknown disturbances including noise errors and model errors, and the outputs have the estimation errors. Then, making the H∞ norm of this dynamic system as a cost function, a fast H∞ filtering algorithm is proposed whose criterion is to guarantee that the worst-cast effect of disturbance on estimation error is smaller than a given boundary. Experimental work was carried out for three typical flow distributions. Results showed that H∞ filter method improves the resolution of the reconstructed images and gains the strong robustness and anti-interference performance in unknown interference noise conditions. In addition, it dramatically reduces the computational time compared with the traditional Gauss-Newton iterative and Kalman filter methods. Therefore, the method is suitable for on-line multiphase flow measurement.

  • Keywords

Electrical Resistance Tomography H∞ Filtering Image Reconstruction

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@Article{JFBI-8-125, author = {}, title = {Fast Image Reconstruction Research Based on H∞ Filtering for Electrical Resistance Tomography}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {1}, pages = {125--132}, abstract = {In order to improve the image reconstructed quality affected by soft filed feature and the speed of dynamic on-line data processing in Electrical Resistance Tomography, we propose a fast image reconstruction algorithm based on H∞ filtering theory. Mainly, on the H∞ filtering principle, a dynamic system is formulated firstly, whose inputs have unknown disturbances including noise errors and model errors, and the outputs have the estimation errors. Then, making the H∞ norm of this dynamic system as a cost function, a fast H∞ filtering algorithm is proposed whose criterion is to guarantee that the worst-cast effect of disturbance on estimation error is smaller than a given boundary. Experimental work was carried out for three typical flow distributions. Results showed that H∞ filter method improves the resolution of the reconstructed images and gains the strong robustness and anti-interference performance in unknown interference noise conditions. In addition, it dramatically reduces the computational time compared with the traditional Gauss-Newton iterative and Kalman filter methods. Therefore, the method is suitable for on-line multiphase flow measurement.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi03201512}, url = {http://global-sci.org/intro/article_detail/jfbi/4692.html} }
TY - JOUR T1 - Fast Image Reconstruction Research Based on H∞ Filtering for Electrical Resistance Tomography JO - Journal of Fiber Bioengineering and Informatics VL - 1 SP - 125 EP - 132 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbi03201512 UR - https://global-sci.org/intro/article_detail/jfbi/4692.html KW - Electrical Resistance Tomography KW - H∞ Filtering KW - Image Reconstruction AB - In order to improve the image reconstructed quality affected by soft filed feature and the speed of dynamic on-line data processing in Electrical Resistance Tomography, we propose a fast image reconstruction algorithm based on H∞ filtering theory. Mainly, on the H∞ filtering principle, a dynamic system is formulated firstly, whose inputs have unknown disturbances including noise errors and model errors, and the outputs have the estimation errors. Then, making the H∞ norm of this dynamic system as a cost function, a fast H∞ filtering algorithm is proposed whose criterion is to guarantee that the worst-cast effect of disturbance on estimation error is smaller than a given boundary. Experimental work was carried out for three typical flow distributions. Results showed that H∞ filter method improves the resolution of the reconstructed images and gains the strong robustness and anti-interference performance in unknown interference noise conditions. In addition, it dramatically reduces the computational time compared with the traditional Gauss-Newton iterative and Kalman filter methods. Therefore, the method is suitable for on-line multiphase flow measurement.
Qifang Liu, Han Yan & Xixiang Zhang. (2019). Fast Image Reconstruction Research Based on H∞ Filtering for Electrical Resistance Tomography. Journal of Fiber Bioengineering and Informatics. 8 (1). 125-132. doi:10.3993/jfbi03201512
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