UAV Classification Based on Deep Learning Fusion of Multidimensional UAV Micro-Doppler Image Features
In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which in...
Gespeichert in:
| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | IEEE geoscience and remote sensing letters |
| container_volume | 21 |
| creator | Chen, Xu Ma, Chunguang Zhao, Chaofan Luo, Yong |
| description | In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which integrates frequency modulated continuous wave (FMCW) radar micro-Doppler signals, cadence-velocity diagram (CVD) signals and cepstrum (CEP) signals. This synthesis culminates in UAV classification with exceptional accuracy, surpassing 97%. In this letter, two deep learning fusion approaches leveraging the ResNet34 network were employed: data-level fusion and feature-level fusion. Empirical results unequivocally highlight the potency of deep learning information fusion—most notably, the fusion of the three spectrograms—exceeding 97% accuracy. This firmly underscores the pivotal role that deep learning fusion techniques play in amplifying precision in UAV target classification. |
| doi_str_mv | 10.1109/LGRS.2024.3371171 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2947827905</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2947827905</sourcerecordid><originalsourceid>FETCH-LOGICAL-c225t-29e475ada4289d679e3853ceb4c811a95f54ebaef7a2f71220ededbd36af58c53</originalsourceid><addsrcrecordid>eNotkFFLwzAUhYMoOKc_wLeAz51J2izJ49zsHHQI6sS3kLU3I6Nra9I--O9t2J7u4Z7D4fAh9EjJjFKinov1x-eMEZbN0lRQKugVmlDOZUK4oNdRZzzhSv7corsQjmRMSikmCHaLb7ysTQjOutL0rm3wiwlQ4VGsADpcgPGNaw44H0J0W4u3Q927yp2giR9T41iydaVvk1XbdTV4vDmZA-AcTD94CPfoxpo6wMPlTtEuf_1aviXF-3qzXBRJyRjvE6YgE9xUJmNSVXOhIJU8LWGflZJSo7jlGewNWGGYFZQxAhVU-yqdG8tlydMpejr3dr79HSD0-tgOflwYNFOZkEwoElP0nBoHh-DB6s67k_F_mhIdaepIU0ea-kIz_Qdx4Ghs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2947827905</pqid></control><display><type>article</type><title>UAV Classification Based on Deep Learning Fusion of Multidimensional UAV Micro-Doppler Image Features</title><source>IEEE Electronic Library (IEL)</source><creator>Chen, Xu ; Ma, Chunguang ; Zhao, Chaofan ; Luo, Yong</creator><creatorcontrib>Chen, Xu ; Ma, Chunguang ; Zhao, Chaofan ; Luo, Yong</creatorcontrib><description>In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which integrates frequency modulated continuous wave (FMCW) radar micro-Doppler signals, cadence-velocity diagram (CVD) signals and cepstrum (CEP) signals. This synthesis culminates in UAV classification with exceptional accuracy, surpassing 97%. In this letter, two deep learning fusion approaches leveraging the ResNet34 network were employed: data-level fusion and feature-level fusion. Empirical results unequivocally highlight the potency of deep learning information fusion—most notably, the fusion of the three spectrograms—exceeding 97% accuracy. This firmly underscores the pivotal role that deep learning fusion techniques play in amplifying precision in UAV target classification.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2024.3371171</identifier><language>eng</language><publisher>Piscataway: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</publisher><subject>Accuracy ; Classification ; Continuous radiation ; Data integration ; Deep learning ; Doppler sonar ; Radar ; Spectrograms ; Unmanned aerial vehicles</subject><ispartof>IEEE geoscience and remote sensing letters, 2024, Vol.21, p.1-5</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c225t-29e475ada4289d679e3853ceb4c811a95f54ebaef7a2f71220ededbd36af58c53</cites><orcidid>0000-0002-7869-3420 ; 0009-0005-4504-8985 ; 0000-0003-0667-7920</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4010,27900,27901,27902</link.rule.ids></links><search><creatorcontrib>Chen, Xu</creatorcontrib><creatorcontrib>Ma, Chunguang</creatorcontrib><creatorcontrib>Zhao, Chaofan</creatorcontrib><creatorcontrib>Luo, Yong</creatorcontrib><title>UAV Classification Based on Deep Learning Fusion of Multidimensional UAV Micro-Doppler Image Features</title><title>IEEE geoscience and remote sensing letters</title><description>In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which integrates frequency modulated continuous wave (FMCW) radar micro-Doppler signals, cadence-velocity diagram (CVD) signals and cepstrum (CEP) signals. This synthesis culminates in UAV classification with exceptional accuracy, surpassing 97%. In this letter, two deep learning fusion approaches leveraging the ResNet34 network were employed: data-level fusion and feature-level fusion. Empirical results unequivocally highlight the potency of deep learning information fusion—most notably, the fusion of the three spectrograms—exceeding 97% accuracy. This firmly underscores the pivotal role that deep learning fusion techniques play in amplifying precision in UAV target classification.</description><subject>Accuracy</subject><subject>Classification</subject><subject>Continuous radiation</subject><subject>Data integration</subject><subject>Deep learning</subject><subject>Doppler sonar</subject><subject>Radar</subject><subject>Spectrograms</subject><subject>Unmanned aerial vehicles</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNotkFFLwzAUhYMoOKc_wLeAz51J2izJ49zsHHQI6sS3kLU3I6Nra9I--O9t2J7u4Z7D4fAh9EjJjFKinov1x-eMEZbN0lRQKugVmlDOZUK4oNdRZzzhSv7corsQjmRMSikmCHaLb7ysTQjOutL0rm3wiwlQ4VGsADpcgPGNaw44H0J0W4u3Q927yp2giR9T41iydaVvk1XbdTV4vDmZA-AcTD94CPfoxpo6wMPlTtEuf_1aviXF-3qzXBRJyRjvE6YgE9xUJmNSVXOhIJU8LWGflZJSo7jlGewNWGGYFZQxAhVU-yqdG8tlydMpejr3dr79HSD0-tgOflwYNFOZkEwoElP0nBoHh-DB6s67k_F_mhIdaepIU0ea-kIz_Qdx4Ghs</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Chen, Xu</creator><creator>Ma, Chunguang</creator><creator>Zhao, Chaofan</creator><creator>Luo, Yong</creator><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-7869-3420</orcidid><orcidid>https://orcid.org/0009-0005-4504-8985</orcidid><orcidid>https://orcid.org/0000-0003-0667-7920</orcidid></search><sort><creationdate>2024</creationdate><title>UAV Classification Based on Deep Learning Fusion of Multidimensional UAV Micro-Doppler Image Features</title><author>Chen, Xu ; Ma, Chunguang ; Zhao, Chaofan ; Luo, Yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c225t-29e475ada4289d679e3853ceb4c811a95f54ebaef7a2f71220ededbd36af58c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Classification</topic><topic>Continuous radiation</topic><topic>Data integration</topic><topic>Deep learning</topic><topic>Doppler sonar</topic><topic>Radar</topic><topic>Spectrograms</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Xu</creatorcontrib><creatorcontrib>Ma, Chunguang</creatorcontrib><creatorcontrib>Zhao, Chaofan</creatorcontrib><creatorcontrib>Luo, Yong</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE geoscience and remote sensing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Xu</au><au>Ma, Chunguang</au><au>Zhao, Chaofan</au><au>Luo, Yong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>UAV Classification Based on Deep Learning Fusion of Multidimensional UAV Micro-Doppler Image Features</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><date>2024</date><risdate>2024</risdate><volume>21</volume><spage>1</spage><epage>5</epage><pages>1-5</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><abstract>In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which integrates frequency modulated continuous wave (FMCW) radar micro-Doppler signals, cadence-velocity diagram (CVD) signals and cepstrum (CEP) signals. This synthesis culminates in UAV classification with exceptional accuracy, surpassing 97%. In this letter, two deep learning fusion approaches leveraging the ResNet34 network were employed: data-level fusion and feature-level fusion. Empirical results unequivocally highlight the potency of deep learning information fusion—most notably, the fusion of the three spectrograms—exceeding 97% accuracy. This firmly underscores the pivotal role that deep learning fusion techniques play in amplifying precision in UAV target classification.</abstract><cop>Piscataway</cop><pub>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</pub><doi>10.1109/LGRS.2024.3371171</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-7869-3420</orcidid><orcidid>https://orcid.org/0009-0005-4504-8985</orcidid><orcidid>https://orcid.org/0000-0003-0667-7920</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1545-598X |
| ispartof | IEEE geoscience and remote sensing letters, 2024, Vol.21, p.1-5 |
| issn | 1545-598X 1558-0571 |
| language | eng |
| recordid | cdi_proquest_journals_2947827905 |
| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Classification Continuous radiation Data integration Deep learning Doppler sonar Radar Spectrograms Unmanned aerial vehicles |
| title | UAV Classification Based on Deep Learning Fusion of Multidimensional UAV Micro-Doppler Image Features |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T20%3A54%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=UAV%20Classification%20Based%20on%20Deep%20Learning%20Fusion%20of%20Multidimensional%20UAV%20Micro-Doppler%20Image%20Features&rft.jtitle=IEEE%20geoscience%20and%20remote%20sensing%20letters&rft.au=Chen,%20Xu&rft.date=2024&rft.volume=21&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.issn=1545-598X&rft.eissn=1558-0571&rft_id=info:doi/10.1109/LGRS.2024.3371171&rft_dat=%3Cproquest_cross%3E2947827905%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2947827905&rft_id=info:pmid/&rfr_iscdi=true |