Abstract:ECT (Electrical Capacitance Tomography)is one ofthe effective methods to measure the parameters of twophase flow The threedimensionalimage reconstruction of capacitance tomography is a sensor system based onmultilayer electrode structure A new redundant sensitivity matrix isestablished to map the relationship between the dielectric constantdistribution and the sensor reading by measuring the capacitance values betweenthe same layer and the heterogeneous plate As a highly nonlinear simplelinearization problem

there are mismatches in the position and size of theobject being imaged To overcome these problems

and the greedy sparsity methodis used to reconstruct the 3d image The redundant sensitivity matrix isconstructed by using ksvd dictionary learning method and nonlinear simulationThe typical structure of the object under test can be better captured bytraining the real data of the redundant sensitivity matrix The proposed methodcan also improve the image quality of the reconstruction by comparing thesimulation experiments