A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks
In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for...
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doaj-5f80d9179848417ca7b983ad9e6153a52021-06-28T23:00:37ZengIEEEIEEE Access2169-35362021-01-019893608937510.1109/ACCESS.2021.30913099461742A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural NetworksJin-Woo Han0https://orcid.org/0000-0003-1208-2325Cheong Hee Park1https://orcid.org/0000-0002-8233-2206Department of Computer Science and Engineering, Chungnam National University, Daejeon, South KoreaDepartment of Computer Science and Engineering, Chungnam National University, Daejeon, South KoreaIn the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments.https://ieeexplore.ieee.org/document/9461742/Multi-task learning(MTL)deep learningPRIdeinterleavingmodulationelectronic warfare |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jin-Woo Han Cheong Hee Park |
spellingShingle |
Jin-Woo Han Cheong Hee Park A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks IEEE Access Multi-task learning(MTL) deep learning PRI deinterleaving modulation electronic warfare |
author_facet |
Jin-Woo Han Cheong Hee Park |
author_sort |
Jin-Woo Han |
title |
A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks |
title_short |
A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks |
title_full |
A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks |
title_fullStr |
A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks |
title_full_unstemmed |
A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks |
title_sort |
unified method for deinterleaving and pri modulation recognition of radar pulses based on deep neural networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments. |
topic |
Multi-task learning(MTL) deep learning PRI deinterleaving modulation electronic warfare |
url |
https://ieeexplore.ieee.org/document/9461742/ |
work_keys_str_mv |
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