A Monte Carlo Technique for Large-Scale Dynamic Tomography
A Monte Carlo Technique for Large-Scale Dynamic Tomography
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大规模动态层析成像的蒙特卡罗技术
DOI：
10.1109/icassp.2007.367062
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
F. Kamalabadi
中科院分区：
文献类型：
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作者：
M. Butala;R. Frazin;Yuguo Chen;F. Kamalabadi
我们解决重建一个物理上不断变化的未知的层析测量，制定它作为一个状态估计问题。在本文中提出的方法是本地集合卡尔曼滤波器（LEnKF），蒙特卡罗状态估计过程，是计算上容易处理的状态维数是大的。我们建立的条件下，LEnKF是等效的高斯粒子滤波器。的LEnKF的性能进行评估，在一个数值例子中，并示出给状态估计的质量几乎相等的最佳卡尔曼滤波器，但在一个95%的计算减少。
We address the reconstruction of a physically evolving unknown from tomographic measurements by formulating it as a state estimation problem. The approach presented in this paper is the localized ensemble Kalman filter (LEnKF); a Monte Carlo state estimation procedure that is computationally tractable when the state dimension is large. We establish the conditions under which the LEnKF is equivalent to the Gaussian particle filter. The performance of the LEnKF is evaluated in a numerical example and is shown to give state estimates of almost equal quality as the optimal Kalman filter but at a 95% reduction in computation.