Detecting Faulty Elements in Antenna Array Using Extreme Learning Machine

This paper proposes a method for diagnosing the position and number of failed elements in antenna array using Extreme Learning Machine. Elements failure will directly change the aperture distribution and seriously affect the normal use of the antenna array. Therefore, it is urgent and necessary to d...

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Bibliographic Details
Published inIEEE Asia-Pacific Conference on Antennas and Propagation pp. 597 - 598
Main Authors JIANG, Xiaochao, JIAO, Anxia, QIN, Jiahui, JIANG, Tao
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.08.2019
Subjects
antenna arrays
Extreme Learning Machine
Extreme learning machines
faulty elements
Machine learning
Machine learning algorithms
Neural networks
pattern
Planar arrays
Simulation
Training
Online AccessGet full text
ISSN2642-9179
DOI10.1109/APCAP47827.2019.9472127

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Summary:This paper proposes a method for diagnosing the position and number of failed elements in antenna array using Extreme Learning Machine. Elements failure will directly change the aperture distribution and seriously affect the normal use of the antenna array. Therefore, it is urgent and necessary to detect the faulty elements when antenna array is damaged. A large number of experiments have shown that Extreme Learning Machine algorithm can be used to locate the faulty elements of antenna arrays from the distortion pattern. Most of the failed elements could be successfully diagnosed by adopting the algorithm.
ISSN:2642-9179
DOI:10.1109/APCAP47827.2019.9472127