Object Localization in Highly Cluttered Environments Using Neural Network Learning on Microwave Scattering Data

ABSTRACT Bifocusing‐based microwave imaging is a promising direct imaging method for object localization in various applications. However, the method has the limitation that it only works well in environments with a homogenous background. In this paper, we present a direct microwave imaging method u...

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Bibliographic Details
Published in:Microwave and optical technology letters 2024-11, Vol.66 (11), p.n/a
Main Authors: Jeong, Janghoon, Park, Won‐Kwang, Son, Seong‐Ho
Format: Article
Language:English
Subjects:
artificial neural network
bifocusing method
Imaging
Localization
microwave imaging
Microwave scattering
Neural networks
object localization
Water tanks
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Summary:ABSTRACT Bifocusing‐based microwave imaging is a promising direct imaging method for object localization in various applications. However, the method has the limitation that it only works well in environments with a homogenous background. In this paper, we present a direct microwave imaging method using a neural network (NN) model that can localize a small object even in highly cluttered environments with strong scatterers. The NN model is trained on some data sets obtained through multistatic measurements in a nonhomogeneous environment. To verify the approach, we prepared an experimental testbed equipped with a water tank containing 3 strong scatterers and 16 antennas to obtain 920 MHz multistatic scattering data. This experiment shows good localization performance, with an average localization error of approximately 4 mm (1/9 of a wavelength) over the entire experimental area, even in a strong scattering background.
ISSN:0895-2477
1098-2760
DOI:10.1002/mop.70020