(untitled) (Journal of Fiber Bioengineering and Informatics, 2015)
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 H221e filtering theory. Mainly, on the H221e 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 H221e norm of this dynamic system as a cost function, a fast H221e 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 H221e 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.