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Title: MUSIC-like algorithm for source localization in electrical impedance tomography
Authors: Borijindargoon, Narong
Ng, Boon Poh
Rahardja, Susanto
Keywords: Electrical Impedance Tomography
Array Processing
Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Borijindargoon, N., Ng, B. P., & Rahardja, S. (2019). MUSIC-like algorithm for source localization in electrical impedance tomography. IEEE Transactions on Industrial Electronics, 66(6), 4661-4671. doi:10.1109/TIE.2018.2863196
Series/Report no.: IEEE Transactions on Industrial Electronics
Abstract: In electrical impedance tomography (EIT), the noise amplified solution caused during matrix inversion can be avoided with nonparametric spectral-based estimation when the conductivity variation is bounded and spatially sparse. Among many spectral-based algorithms used in direction-of-arrival estimation, an algorithm called multiple signal classification (MUSIC) is one of the most well-known algorithms that has super resolution performance. However, its dependence on the model-order estimation can lead to performance degradation, especially for quasi-static environment, such as EIT application, and this is due to source location changes and conductivity variation. In this paper, the relationship between source position, conductivity variation, ill-conditioned array manifold, and eigenvalues of the covariance matrix are explored. An algorithm called MUSIC-like, which has high resolution performance comparable to MUSIC, is then proposed for EIT application. It is formulated under the beamforming framework and, therefore, does not require an estimation of model order from the covariance matrix. Simulation results show that the proposed method is capable of obtaining high resolution performance under various noise levels. An 8-electrode EIT system prototype was built using the proposed method, and experimental results confirm the high resolution performance capability of the proposed method.
ISSN: 0278-0046
DOI: 10.1109/TIE.2018.2863196
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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