Influence of Parameters in Kalman-filter-based Method on Image Quality for Electrical Capacitance Tomography

Abstract

As a powerful tool to get a recursive solution of least squares estimation, the Kalman filter has been used for image reconstruction in Electrical Capacitance Tomography (ECT). In the Kalman-filter-based image reconstruction method, some key parameters, e.g., initial guess, observation noise covariance and initial estimate error covariance, greatly influence the performance of the method. Inappropriate values of these parameters may cause a series of problems, such as lower convergence rate, artifacts, or filter divergence. This paper aims to analyze the influence of the parameters on the image quality for ECT and guide the selection of the parameters. Numerical simulation and experiment were carried out and the results show that with an initial guess obtained by linear back projection (LBP) method and a good match of observation noise covariance and initial estimate error covariance, the performance of the Kalman-filter-based method can be improved.