Nonstationary phase boundary estimation in electrical impedance tomography using unscented Kalman filter

In this paper, an effective nonstationary phase boundary estimation scheme in electrical impedance tomography is presented based on the unscented Kalman filter. The inverse problem is treated as a stochastic nonlinear state estimation problem with the nonstationary phase boundary (state) being estimated online with the aid of unscented Kalman filter. This research targets the industrial applications, such as imaging of stirrer vessel for detection of air distribution or detecting large air bubbles in pipelines. Within the domains, there exist “voids” having zero conductivity. The design variables for phase boundary estimation are truncated Fourier coefficients. Computer simulations and experimental results are provided to evaluate the performance of unscented Kalman filter in comparison with extended Kalman filter to show a better performance of the unscented Kalman filter approach.