Image Reconstruction Based on Fuzzy Adaptive Kalman Filter in Electrical Capacitance Tomography

In this article, a fuzzy adaptive Kalman filter (FAKaF)-based method was proposed for image reconstruction in electrical capacitance tomography (ECT). When the Kalman filter (KF) is applied for image reconstruction in ECT, two key parameters need to be predetermined, i.e., the observation noise covariance (R) and the initial estimation error covariance (P-0). These two parameters play significant roles in image reconstruction. For instance, a larger R may lead to a blurrier image. A larger P-0 can cause increasing artifacts or even heavier distortion of the reconstructed image. In this work, a FAKaF was established to adjust P-0 using R calculated from the measured capacitances so as to improve the quality of the reconstructed image. The implementation of the FAKaF-based reconstruction method was divided into offline and online parts. In the offline part, the Kalman gain and the corresponding fuzzy control table were precalculated, aiming to save resource consumption and improve imaging speed. Simulations and experiments were carried out to evaluate the image quality and computational cost of the proposed method. Comparisons were made with three widely-used algorithms. Results show that the proposed FAKaF-based method yields good quality images and few artifacts, needs few iterations and consumes less computational cost.

Automated summary

A fuzzy adaptive Kalman filter (FAKaF)-based method for image reconstruction in electrical capacitance tomography (ECT) was proposed, which adjusts covariance parameters to improve image quality. Simulations and experiments show that this method performs well in terms of image quality and computational cost.