Reconstruction Algorithm In Electrical Capacitance Tomography By Bayesian Inference

Electrical Capacitance Tomography(ECT)is a promising technique of process tomography,which can obtain the distribution of the media image in an enclosed area. It has the advantage of being non-intrusive, broad application, and low price, etc, so it has been widely used in a variety of industrial processes.Traditional ECT image reconstruction algorithm can only obtain the approximate optimal solution of the dielectric constant, but the sampling method based on Bayesian games can be distributed within a comprehensive description of the dielectric constant.In this paper, based on Bayesian theory, we construct a new image reconstruction algorithm for ECT. Firstly, in the forword problem, we obtain the distribution of the potential field within the media by finite element calculations.Secondly, in the inverse problem, based on Bayesian theory and priori information of standard normal distribution, we obtain the posterior distribution model of the dielectric constant of ECT.Finally, we introduce the linear back-projection as the initial state of Markov chain,sample the posterior distribution with the application of Metropolis-Hastings sampling algorithm,and reconstruct the image by expectations based on samples.Then we obtain variance imaging results of different proposal distribution.