Abstract
In the derivation of the sensitivity matrix of electrical capacitance tomography, only the linear portion of the sensitivity coefficient is usually retained, while the neglected nonlinear part also contains important imaging information. In order to improve the accuracy of image reconstruction, a second-order hybrid sensitivity matrix is presented based on the capacitance normalization model and second-order item of sensitivity coefficients in this paper. Then, a fuzzy nonlinear programming algorithm based on the second-order hybrid sensitivity matrix (SHS-FNP) is proposed. Simulation and experiments are carried out. Reconstructed images using the presented method are compared with those of the Tikhonov algorithm, first-order hybrid Landweber algorithm, second-order hybrid Landweber algorithm, and fuzzy linear programming algorithm. The experimental results show that the SHS-FNP algorithm considerably enhances the quality of the reconstructed image.
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