Fast Image Reconstruction Research Based on H∞ Filtering for Electrical Resistance Tomography

Abstract

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