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State estimation in process tomography—Three‐dimensional impedance imaging of moving fluids
Author(s) -
Seppänen A.,
Vauhkonen M.,
Vauhkonen P. J.,
Voutilainen A.,
Kaipio J. P.
Publication year - 2007
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.2142
Subject(s) - electrical impedance tomography , kalman filter , representation (politics) , tomography , algorithm , state space representation , state (computer science) , computer science , process (computing) , electrical impedance , iterative reconstruction , artificial intelligence , physics , optics , quantum mechanics , politics , law , political science , operating system
In this paper, we consider three‐dimensional impedance imaging of rapidly varying objects. We especially concentrate on the case where the target is a moving fluid and the objective is to track the concentration distribution of a substance dissolved in the fluid. The observations are made as in ordinary electrical impedance tomography (EIT), but in the reconstruction we employ the convection–diffusion model to yield information on the temporal behavior of the object. The observation model of EIT together with the evolution model constitute the state‐space representation of the system, and the reconstruction problem of EIT can be described as a state estimation problem. The state estimation problem is solved using the Kalman filter and the fixed‐interval smoother algorithms. The performance of the state estimation approach is evaluated in a simulation study. Copyright © 2007 John Wiley & Sons, Ltd.