Automatic recognition of pulse repetition interval modulation using temporal convolutional network

Abstract Pulse Repetition Interval (PRI) modulation recognition is a key issue in radar identification process in modern electronic intelligent (ELINT) and electronic support measure (ESM) systems. In this study, a novel approach based on the intrinsic property of the temporal convolutional network...

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Autores principales:	Abolfazl Dadgarnia, Mohammad Taghi Sadeghi
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/9095b0a8dadf4849acc6e506732d16ae
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id	oai:doaj.org-article:9095b0a8dadf4849acc6e506732d16ae
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spelling	oai:doaj.org-article:9095b0a8dadf4849acc6e506732d16ae2021-11-09T10:16:47ZAutomatic recognition of pulse repetition interval modulation using temporal convolutional network1751-96831751-967510.1049/sil2.12069https://doaj.org/article/9095b0a8dadf4849acc6e506732d16ae2021-12-01T00:00:00Zhttps://doi.org/10.1049/sil2.12069https://doaj.org/toc/1751-9675https://doaj.org/toc/1751-9683Abstract Pulse Repetition Interval (PRI) modulation recognition is a key issue in radar identification process in modern electronic intelligent (ELINT) and electronic support measure (ESM) systems. In this study, a novel approach based on the intrinsic property of the temporal convolutional network (TCN) is presented for PRI modulation type recognition. Since a causal TCN is used for this purpose, the method is suitable for online ESM and ELINT analysis. The simulation results show that the method accurately classifies seven types of PRI modulation including simple, dwell and switch, stagger, jitter, agile, sliding and periodic modulations in realistic scenarios. In order to investigate the performance of the method when the problem of missing or spurious pulses occurs, the impacts of these problems are examined, separately and together, on the recognition process. It is shown that the method works effectively even with a relatively high percentage of missing and/or spurious pulses (up to 30%). The method also works effectively in the presence of unintentional jitter and large outliers, which can be due to radar antenna scan. The experimental results on real data confirm that the proposed method performs accurately and effectively in real world scenarios.Abolfazl DadgarniaMohammad Taghi SadeghiWileyarticleTelecommunicationTK5101-6720ENIET Signal Processing, Vol 15, Iss 9, Pp 633-648 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Abolfazl Dadgarnia Mohammad Taghi Sadeghi Automatic recognition of pulse repetition interval modulation using temporal convolutional network
description	Abstract Pulse Repetition Interval (PRI) modulation recognition is a key issue in radar identification process in modern electronic intelligent (ELINT) and electronic support measure (ESM) systems. In this study, a novel approach based on the intrinsic property of the temporal convolutional network (TCN) is presented for PRI modulation type recognition. Since a causal TCN is used for this purpose, the method is suitable for online ESM and ELINT analysis. The simulation results show that the method accurately classifies seven types of PRI modulation including simple, dwell and switch, stagger, jitter, agile, sliding and periodic modulations in realistic scenarios. In order to investigate the performance of the method when the problem of missing or spurious pulses occurs, the impacts of these problems are examined, separately and together, on the recognition process. It is shown that the method works effectively even with a relatively high percentage of missing and/or spurious pulses (up to 30%). The method also works effectively in the presence of unintentional jitter and large outliers, which can be due to radar antenna scan. The experimental results on real data confirm that the proposed method performs accurately and effectively in real world scenarios.
format	article
author	Abolfazl Dadgarnia Mohammad Taghi Sadeghi
author_facet	Abolfazl Dadgarnia Mohammad Taghi Sadeghi
author_sort	Abolfazl Dadgarnia
title	Automatic recognition of pulse repetition interval modulation using temporal convolutional network
title_short	Automatic recognition of pulse repetition interval modulation using temporal convolutional network
title_full	Automatic recognition of pulse repetition interval modulation using temporal convolutional network
title_fullStr	Automatic recognition of pulse repetition interval modulation using temporal convolutional network
title_full_unstemmed	Automatic recognition of pulse repetition interval modulation using temporal convolutional network
title_sort	automatic recognition of pulse repetition interval modulation using temporal convolutional network
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/9095b0a8dadf4849acc6e506732d16ae
work_keys_str_mv	AT abolfazldadgarnia automaticrecognitionofpulserepetitionintervalmodulationusingtemporalconvolutionalnetwork AT mohammadtaghisadeghi automaticrecognitionofpulserepetitionintervalmodulationusingtemporalconvolutionalnetwork
_version_	1718441129014198272

Automatic recognition of pulse repetition interval modulation using temporal convolutional network

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