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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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12069 |
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| _version_ | 1832546747632582656 |
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| author | Abolfazl Dadgarnia Mohammad Taghi Sadeghi |
| author_facet | Abolfazl Dadgarnia Mohammad Taghi Sadeghi |
| author_sort | Abolfazl Dadgarnia |
| collection | DOAJ |
| 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 |
| id | doaj-art-9095b0a8dadf4849acc6e506732d16ae |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-9095b0a8dadf4849acc6e506732d16ae2025-02-03T06:47:26ZengWileyIET Signal Processing1751-96751751-96832021-12-0115963364810.1049/sil2.12069Automatic recognition of pulse repetition interval modulation using temporal convolutional networkAbolfazl Dadgarnia0Mohammad Taghi Sadeghi1Department of Electrical Engineering Yazd University Yazd IranDepartment of Electrical Engineering Yazd University Yazd IranAbstract 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.https://doi.org/10.1049/sil2.12069jitterpulse modulationradar antennasradar computingradar signal processingconvolutional neural nets |
| spellingShingle | Abolfazl Dadgarnia Mohammad Taghi Sadeghi Automatic recognition of pulse repetition interval modulation using temporal convolutional network IET Signal Processing jitter pulse modulation radar antennas radar computing radar signal processing convolutional neural nets |
| title | 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_short | Automatic recognition of pulse repetition interval modulation using temporal convolutional network |
| title_sort | automatic recognition of pulse repetition interval modulation using temporal convolutional network |
| topic | jitter pulse modulation radar antennas radar computing radar signal processing convolutional neural nets |
| url | https://doi.org/10.1049/sil2.12069 |
| work_keys_str_mv | AT abolfazldadgarnia automaticrecognitionofpulserepetitionintervalmodulationusingtemporalconvolutionalnetwork AT mohammadtaghisadeghi automaticrecognitionofpulserepetitionintervalmodulationusingtemporalconvolutionalnetwork |