Application of MUSIC algorithm in real-world microwave imaging of unknown anomalies from scattering matrix

Park, Won-Kwang

doi:10.1016/j.ymssp.2020.107501

Cited by 27 publications

(9 citation statements)

References 60 publications

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“…Moreover, the analysis of the indicator function when the sets of observation and incident directions are uniformly distributed will be the forthcoming work. Finally, applications to real-world microwave imaging [30,35,52] and extending matters to include the three-dimensional problem [53][54][55] will also prove to be interesting approaches.…”

Section: Discussionmentioning

confidence: 99%

Fast Localization of Small Inhomogeneities from Far-Field Pattern Data in the Limited-Aperture Inverse Scattering Problem

Park

2021

Mathematics

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In this study, we consider a sampling-type algorithm for the fast localization of small electromagnetic inhomogeneities from measured far-field pattern data in the limited-aperture inverse scattering problem. For this purpose, we designed an indicator function based on the structure of left- and right-singular vectors of a multistatic response matrix, the elements of which were measured far-field pattern data. We then rigorously investigated the mathematical structure of the indicator function in terms of purely dielectric permittivity and magnetic permeability contrast cases by establishing a relationship with an infinite series of Bessel functions of an integer order of the first kind and a range of incident and observation directions before exploring various intrinsic properties of the algorithm, including its feasibility and limitations. Simulation results with synthetic data corrupted by random noise are presented to support the theoretical results.

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Section: Discussionmentioning

confidence: 99%

Fast Localization of Small Inhomogeneities from Far-Field Pattern Data in the Limited-Aperture Inverse Scattering Problem

Park

2021

Mathematics

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“…It is the critical technology of array signal processing, and the purpose is to estimate the transmitter positions of the signals received by arrays. e representative conventional DOA estimation algorithms are the estimation of signal parameters via rotational invariance techniques (ESPRIT) [4,5] and multiple signal classification (MUSIC) [6,7]. ESPRIT uses two subarrays with translation invariance to realize DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

Chen

Joo

2022

International Journal of Antennas and Propagation

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This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.

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“…Therefore, by analyzing the changes in the RF signal, the location of the target can be determined. Other methods include ultra-wideband tomographic radar imaging [4] and multistatic microwave imaging [5]. These methods use the scattering parameters measured.…”

Section: Introductionmentioning

confidence: 99%

“…These methods use the scattering parameters measured. The reconstructed image shows the distribution of the dielectric properties of an object or localizes small anomalies that are stationary or moving [5].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Microwave Imaging Method for Object Localization

Son

Park

2022

J Electromagn Eng Sci

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This paper presents a new microwave imaging method using artificial neural networks to localize an object. The trained neural network reconstructs a tomographic image from the measured scattering data, such as a nonlinear electromagnetic inverse scattering solver. The appropriate number of hidden neurons is determined through the cross-entropy between network predictions and target values. To verify this method experimentally, we set up a testbed consisting of 16 antennas that transmit and receive 950 MHz microwaves underwater and used a metal rod with a diameter of 2 mm as a localizing target. The results show excellent imaging performance with fewer artifacts and less than a 2-mm localization error.

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Application of MUSIC algorithm in real-world microwave imaging of unknown anomalies from scattering matrix

Cited by 27 publications

References 60 publications

Fast Localization of Small Inhomogeneities from Far-Field Pattern Data in the Limited-Aperture Inverse Scattering Problem

Fast Localization of Small Inhomogeneities from Far-Field Pattern Data in the Limited-Aperture Inverse Scattering Problem

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

A Neural Network-Based Microwave Imaging Method for Object Localization

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