Remote Diagnosis of Fault Element in Active Phased Arrays using Deep Neural Network

Martin Hedegaard Nielsen, Mads Helge Jespersen, Ming Shen

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11 Citationer (Scopus)

Abstract

It can be extremely mission-critical to identify the source of an error in satellite communication systems and in this initial work an approach based on neural networks is proposed for diagnosing the faulty antenna elements remotely from the receiver side. A simple active antenna array which consists of four power amplifiers and a four-by-one linear antenna array has been considered in this work for proof of concept. Signals captured from various fault scenarios are used to train a 4layered feed-forward neural network using amplitude and phase data. The validation results show that the trained neural network can correctly identify the fault power amplifier with an accuracy higher than 96 %, which indicates the promising potential of the proposed approach.

OriginalsprogEngelsk
Titel2019 27th Telecommunications Forum (TELFOR)
Antal sider4
ForlagIEEE (Institute of Electrical and Electronics Engineers)
Publikationsdato2020
Artikelnummer8971339
ISBN (Trykt)978-1-7281-4790-1
ISBN (Elektronisk)978-1-7281-4790-1
DOI
StatusUdgivet - 2020
Begivenhed2019 27th Telecommunications Forum (TELFOR) - Beograd, Serbien
Varighed: 26 nov. 201927 nov. 2019

Konference

Konference2019 27th Telecommunications Forum (TELFOR)
Land/OmrådeSerbien
ByBeograd
Periode26/11/201927/11/2019
NavnProceedings of the IEEE Telecommunications Forum (TELFOR)

Emneord

  • Machine learning
  • Satellite communications
  • Neural Network
  • Antenna array
  • Antennas

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