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.

中文翻译：

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用于阻抗断层扫描中源定位的类音乐算法

在电阻抗断层扫描 (EIT) 中，当电导率变化有界且空间稀疏时，可以通过基于非参数谱的估计来避免矩阵求逆过程中引起的噪声放大解。在用于到达方向估计的许多基于频谱的算法中，一种称为多信号分类（MUSIC）的算法是最著名的具有超分辨率性能的算法之一。然而，它对模型阶次估计的依赖会导致性能下降，特别是对于准静态环境，如 EIT 应用，这是由于源位置变化和电导率变化造成的。本文探讨了源位置、电导率变化、病态阵列流形和协方差矩阵特征值之间的关系。然后提出了一种称为 MUSIC-like 的算法，它具有与 MUSIC 相当的高分辨率性能，然后被提出用于 EIT 应用。它是在波束成形框架下制定的，因此不需要从协方差矩阵中估计模型阶数。仿真结果表明，所提出的方法能够在各种噪声水平下获得高分辨率性能。使用该方法构建了一个 8 电极 EIT 系统原型，实验结果证实了该方法的高分辨率性能。仿真结果表明，所提出的方法能够在各种噪声水平下获得高分辨率性能。使用该方法构建了一个 8 电极 EIT 系统原型，实验结果证实了该方法的高分辨率性能。仿真结果表明，所提出的方法能够在各种噪声水平下获得高分辨率性能。使用该方法构建了一个 8 电极 EIT 系统原型，实验结果证实了该方法的高分辨率性能。