Recognition of Radar Emitters with Agile Waveform Based on Hybrid Deep Neural Network and Attention Mechanism

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dc.contributor.authorFeng, Yuntian
dc.contributor.authorWang, Guoliang
dc.contributor.authorLiu, Zhipeng
dc.contributor.authorCui, Bo
dc.contributor.authorYang, Yu
dc.contributor.authorXu, Xiong
dc.contributor.authorHan, Hui
dc.coverage.issue4cs
dc.coverage.volume30cs
dc.date.accessioned2021-12-13T09:46:53Z
dc.date.available2021-12-13T09:46:53Z
dc.date.issued2021-12cs
dc.description.abstractWith the increasing complexity of the electromagnetic environment and the continuous development of radar technology, more and more modern digital programmable radars using agile waveform will appear in the future battlefield. It is difficult to effectively identify these radar emitters with complex system only by relying on traditional recognition models. In response to the above problem, this paper proposes a recognition method of radar emitters with agile waveform based on hybrid deep neural network and attention mechanism to deal with the problem of variable conventional characteristic parameters of radar emitter signals with agile waveform. First, we perform a distributed representation of the pulse signal data to generate high-dimensional sparse signal features. Then we design to use a dynamic Convolutional Neural Network to extract features of structural details of radar emitter signals with agile waveform at different levels, and use a Long Short-Term Memory to extract its timing features. In order to obtain the deep features that can characterize the agility of the waveform, the attention mechanism-based method is used to fuse the extracted structural features and timing features, and at the same time it can reduce the influence of noise in complex electromagnetic environment on the characteristic data of radar emitter. Finally, the deep feature is input into the Softmax layer to complete the recognition of radar emitters with agile waveform. The experimental results show that the method proposed in this paper can effectively solve the problem of the recognition of radar emitters with agile waveform, and the recognition accuracy is improved by 1.26% compared with the traditional models and other deep models.en
dc.formattextcs
dc.format.extent704-712cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2021 vol. 30, č. 4, s. 704-712. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2021.0704en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/203178
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2021/21_04_0704_0712.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAgility waveformen
dc.subjectradar emitteren
dc.subjecthybrid deep neural networken
dc.subjectattention mechanismen
dc.titleRecognition of Radar Emitters with Agile Waveform Based on Hybrid Deep Neural Network and Attention Mechanismen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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