Image reconstruction in time-varying electrical impedance tomography based on the extended Kalman filter

In electrical impedance tomography (EIT), the resistivity (conductivity) distribution of the unknown object is estimated from boundary voltages induced by different current patterns with the aid of various reconstruction algorithms. In this paper, we propose an EIT image reconstruction algorithm based on the extended Kalman filter (EKF) to estimate rapidly time-varying changes in resistivity occurring within the time taken to acquire a full set of independent measurement data. The EIT inverse problem is formulated as a state estimation problem in which the system is modelled with the state equation and the observation equation. The unknown time-varying state (resistivity) is estimated with the aid of the EKF. Both computer simulations with synthetic data and experiments with real measurement data are provided to illustrate the reconstruction performance of the proposed algorithm.