融合电容层析成像先验信息的集合卡尔曼滤波统计估计

提出融合先验信息的电容层析成像(ECT)系统信息融合成像法——基于Bayesian重建算法的集合卡尔曼滤波融合算法(EnKF)。其优势在于能充分利用流体动力方面的先验信息,进一步修正原始迭代算法的重建图像质量。Bayesian重建算法在重建图像的迭代过程充分考虑噪声和图像的概率分布,完成初步成像过程。集合卡尔曼滤波算法利用先验信息实现对多相流流体流动的预测,继而改善成像质量。此外,为了便于估算误差的协方差,多相流的相分布作为EnKF融合方法的估计对象,用状态向量空间的灰度差分统计描述,明显改善了通过状态向量空间模型获得的统计估算结果。仿真和试验的结果都充分表明了在ECT系统中集合卡尔曼滤波融合方法的可行性。

Statistical estimation of ensemble Kalman filter fusion based on priori information approach in electrical capacitance tomography

A novel information fusion method,i.e.fusion with priori information approach in electrical capacitance tomography(ECT) ensemble Kalman filtering(EnKF) fusion algorithm based on Bayesian reconstruction algorithm was proposed,which can make full use of fluid dynamic priori information,and then can further revise the reconstruction picture quality iterated by the primitive algorithm.During the iterative computation process,Bayesian algorithm takes the probability of measurement noise and phase distribution into account to complete the preliminary images.EnKF fusion algorithm uses the priori information to achieve the prediction of multiphase flow fluid,and then can improve the image quality.Moreover,the phase distributions estimated by EnKF fusion algorithm were statistically described as the state vector of spatial gray gradients.The statistical estimate results obtained via the state vector space model are significantly improved.Both simulation and experiments clearly show the EnKF fusion algorithm is accessible in the ECT system image reconstruction.