MUSIC-Like Algorithm for Source Localization in Electrical Impedance Tomography

Abstract

In electrical impedance tomography (EIT), the noise amplified solution caused during matrix inversion can be avoided with non-parametric spectral based estimation when the conductivity variation is bounded and spatially sparse. Among many spectral based algorithms used in direction-of-arrival (DOA) estimation, an algorithm called multiple signal classification (MUSIC) is one of the most well-known that has super resolution performance. However, its dependency on the model order estimation can lead to performance degradation especially for quasi-static environment like 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 MUSIClike 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.

Electrical Impedance Tomography Based on a High-resolution Direction-of-arrival Estimation Algorithm

“…We consider the framework of the model proposed by Borijindargoon. (8) Figure 1 shows a 16-electrode EIT system based on the method of adjacent current injection and voltage measurement.…”

“…The change in the impedance of the Pth pixel in the domain produces a disturbance potential Δϕ. A snapshot of the signal received at the boundary can be modeled linearly as (8)…”

“…Borijindargoon introduced a mathematical theory for spatial spectrum estimation into the EIT model, then applied a MUSIC-like algorithm to solve the EIT inverse problem. (8) Although a MUSIC-like algorithm has a clear positioning and imaging result for a target, there are more artifacts of other signals in the imaging domain, especially when the signal-to-noise ratio (SNR) is low.…”

DIRECTIONAL ADAPTIVE MUSIC-LIKE ALGORITHM UNDER SYMMETRIC Α-Stable DISTRIBUTED NOISE

“…Until recently [11], [18], the value of β has been set according to the SNR of the obtained data (data-dependent), where it is fixed throughout all look directions (β in Fig. 6).…”

“…The MUSIC algorithm [2] is a well-known eigenstructure-based method with super resolution performance [3] which has been employed in various contemporary applications from automotive radars [4], unmanned aerial vehicle (UAV) localization [5], and underwater sonar to problems in joint sparse signal recovery [6]- [8] and anomaly detection [9]- [11]. In certain scenarios where an impulse noise is present, the characteristic exponent is less than 2 (α < 2), and hence it is known that only moments of order less than or equal to α is finite [1].…”