Statistical Estimation of the Ensemble Kalman Filter Fusion Based on priori Information Approach in ECT

Minhao Zhou, Fengmin Xiao, Jiangtao Chen, Jing Lei, Shi Liu

Innovative Research Team of “Program for Changjiang Scholars and Innovative Research Team in University”, North China Electric Power University, Beijing 102206, China

Department of energy and power engineering, North China Electric Power University, Beijing, 102206

ABSTRACT

Electrical capacitance tomography (ECT) is a visualized monitoring technique that aims at extracting process characteristics of multi-phase flows in inaccessible pipelines. Since most of real flow processes possess the property of viscidity and continuity, the spatial materials phases are considered to be a logical distribution, while there have gained some measurement data of fluid flow in the multi- model of sensor systems, which are defined as priori information of fluid dynamics. In this contribution a novel Information Fusion Method (IFM) in the presence of priori information of fluid dynamics based approach to solve the underlying inverse ECT problem is proposed. The method improves the quality of reconstruction images obtained by conventionally iterative algorithms in ECT which is limited to the finite capacitance data. In this paper, the information fusion that the Ensemble Kalman Filter (EnKF) based on Bayesian reconstruction algorithm is proposed. Bayesian algorithm, taking the probability of measurement noise and phase distribution into account, carries out the iterative compution. At the same time, an adaptive correction of the filter resulting in information fusion is proposed that significantly improves filter convergence of the applied Ensemble Kalman Filter (EnKF). Furthermore, Phase distributions are described as gray gradients in the state-space which are estimated by the EnKF and the state vector of each sensor is an ensemble member in order to estimate the relative permittivity values involved. As a remarkable result, the statistical estimation result is obtained via the state space model. The ensemble Kalman filter is suboptimal estimator regularization where the error statistics are predicted by using ensemble integration. From the simulation and experiment, it is easy to find that the method is accessible.

Keywords statistical estimation, prior information, ensemble Kalman filter, ECT