Research on Radar Emitters Classification with Fuzzy Support Vector Machines
In this paper, a novel method based on kernel principle component analysis is proposed to extract features of radar emitter signals image of Choi-Williams distribution. Then these discriminative and low dimensional features obtained were fed to the classifier designed for different radar LFM signals...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 164 |
|---|---|
| container_issue | |
| container_start_page | 161 |
| container_title | |
| container_volume | 1 |
| creator | Meng Yafeng Ren Mingqiu Cai Jinyan Han Chunhui |
| description | In this paper, a novel method based on kernel principle component analysis is proposed to extract features of radar emitter signals image of Choi-Williams distribution. Then these discriminative and low dimensional features obtained were fed to the classifier designed for different radar LFM signals which is based on fuzzy support vector machines (FSVMs). In simulation experiments, the classifier attains over 90% overall average correct classification rate. Experimental results show that the proposed FSVM classifier is efficient for different complex radar signals detection and classification. |
| doi_str_mv | 10.1109/IFITA.2009.560 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5231552</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5231552</ieee_id><sourcerecordid>5231552</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-f00f8a726d66c84ce4826899eb75e2102f7c2df2c4d48e4b144cf73d886cebc43</originalsourceid><addsrcrecordid>eNotjM1KAzEYRQNSUGu3btzkBTp-yeR3WYaOFkaEWt2WNPOFRvpHkiLt0zugd3HO4sAl5JFBxRjY50W7WM0qDmArqeCGTKw2oJWVtQLgI3I_JGMZs0bdkknO3zBMSiM43JFuiRld8lt6PNCl612i830sBVOmzc7lHEP0rsSh_sSype35er3Qj_PpdEyFfqEvx0TfnN_GA-YHMgpul3Hy7zH5bOer5nXavb8smlk3jUzLMg0AwTjNVa-UN8KjMFwZa3GjJXIGPGjP-8C96IVBsWFC-KDr3hjlceNFPSZPf78REdenFPcuXdaS10wO-AVmpk6T</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Research on Radar Emitters Classification with Fuzzy Support Vector Machines</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Meng Yafeng ; Ren Mingqiu ; Cai Jinyan ; Han Chunhui</creator><creatorcontrib>Meng Yafeng ; Ren Mingqiu ; Cai Jinyan ; Han Chunhui</creatorcontrib><description>In this paper, a novel method based on kernel principle component analysis is proposed to extract features of radar emitter signals image of Choi-Williams distribution. Then these discriminative and low dimensional features obtained were fed to the classifier designed for different radar LFM signals which is based on fuzzy support vector machines (FSVMs). In simulation experiments, the classifier attains over 90% overall average correct classification rate. Experimental results show that the proposed FSVM classifier is efficient for different complex radar signals detection and classification.</description><identifier>ISBN: 9780769536002</identifier><identifier>ISBN: 076953600X</identifier><identifier>DOI: 10.1109/IFITA.2009.560</identifier><identifier>LCCN: 2008911986</identifier><language>eng</language><publisher>IEEE</publisher><subject>classification ; Feature extraction ; FSVM ; Image analysis ; Information technology ; Kernel ; Radar applications ; Radar detection ; Radar imaging ; radar signal ; Support vector machine classification ; Support vector machines ; Time frequency analysis ; time-frequency transforms</subject><ispartof>2009 International Forum on Information Technology and Applications, 2009, Vol.1, p.161-164</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5231552$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5231552$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Meng Yafeng</creatorcontrib><creatorcontrib>Ren Mingqiu</creatorcontrib><creatorcontrib>Cai Jinyan</creatorcontrib><creatorcontrib>Han Chunhui</creatorcontrib><title>Research on Radar Emitters Classification with Fuzzy Support Vector Machines</title><title>2009 International Forum on Information Technology and Applications</title><addtitle>IFITA</addtitle><description>In this paper, a novel method based on kernel principle component analysis is proposed to extract features of radar emitter signals image of Choi-Williams distribution. Then these discriminative and low dimensional features obtained were fed to the classifier designed for different radar LFM signals which is based on fuzzy support vector machines (FSVMs). In simulation experiments, the classifier attains over 90% overall average correct classification rate. Experimental results show that the proposed FSVM classifier is efficient for different complex radar signals detection and classification.</description><subject>classification</subject><subject>Feature extraction</subject><subject>FSVM</subject><subject>Image analysis</subject><subject>Information technology</subject><subject>Kernel</subject><subject>Radar applications</subject><subject>Radar detection</subject><subject>Radar imaging</subject><subject>radar signal</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Time frequency analysis</subject><subject>time-frequency transforms</subject><isbn>9780769536002</isbn><isbn>076953600X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjM1KAzEYRQNSUGu3btzkBTp-yeR3WYaOFkaEWt2WNPOFRvpHkiLt0zugd3HO4sAl5JFBxRjY50W7WM0qDmArqeCGTKw2oJWVtQLgI3I_JGMZs0bdkknO3zBMSiM43JFuiRld8lt6PNCl612i830sBVOmzc7lHEP0rsSh_sSype35er3Qj_PpdEyFfqEvx0TfnN_GA-YHMgpul3Hy7zH5bOer5nXavb8smlk3jUzLMg0AwTjNVa-UN8KjMFwZa3GjJXIGPGjP-8C96IVBsWFC-KDr3hjlceNFPSZPf78REdenFPcuXdaS10wO-AVmpk6T</recordid><startdate>200905</startdate><enddate>200905</enddate><creator>Meng Yafeng</creator><creator>Ren Mingqiu</creator><creator>Cai Jinyan</creator><creator>Han Chunhui</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200905</creationdate><title>Research on Radar Emitters Classification with Fuzzy Support Vector Machines</title><author>Meng Yafeng ; Ren Mingqiu ; Cai Jinyan ; Han Chunhui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f00f8a726d66c84ce4826899eb75e2102f7c2df2c4d48e4b144cf73d886cebc43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>classification</topic><topic>Feature extraction</topic><topic>FSVM</topic><topic>Image analysis</topic><topic>Information technology</topic><topic>Kernel</topic><topic>Radar applications</topic><topic>Radar detection</topic><topic>Radar imaging</topic><topic>radar signal</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>Time frequency analysis</topic><topic>time-frequency transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Meng Yafeng</creatorcontrib><creatorcontrib>Ren Mingqiu</creatorcontrib><creatorcontrib>Cai Jinyan</creatorcontrib><creatorcontrib>Han Chunhui</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Meng Yafeng</au><au>Ren Mingqiu</au><au>Cai Jinyan</au><au>Han Chunhui</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Research on Radar Emitters Classification with Fuzzy Support Vector Machines</atitle><btitle>2009 International Forum on Information Technology and Applications</btitle><stitle>IFITA</stitle><date>2009-05</date><risdate>2009</risdate><volume>1</volume><spage>161</spage><epage>164</epage><pages>161-164</pages><isbn>9780769536002</isbn><isbn>076953600X</isbn><abstract>In this paper, a novel method based on kernel principle component analysis is proposed to extract features of radar emitter signals image of Choi-Williams distribution. Then these discriminative and low dimensional features obtained were fed to the classifier designed for different radar LFM signals which is based on fuzzy support vector machines (FSVMs). In simulation experiments, the classifier attains over 90% overall average correct classification rate. Experimental results show that the proposed FSVM classifier is efficient for different complex radar signals detection and classification.</abstract><pub>IEEE</pub><doi>10.1109/IFITA.2009.560</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9780769536002 |
| ispartof | 2009 International Forum on Information Technology and Applications, 2009, Vol.1, p.161-164 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5231552 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | classification Feature extraction FSVM Image analysis Information technology Kernel Radar applications Radar detection Radar imaging radar signal Support vector machine classification Support vector machines Time frequency analysis time-frequency transforms |
| title | Research on Radar Emitters Classification with Fuzzy Support Vector Machines |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T11%3A23%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Research%20on%20Radar%20Emitters%20Classification%20with%20Fuzzy%20Support%20Vector%20Machines&rft.btitle=2009%20International%20Forum%20on%20Information%20Technology%20and%20Applications&rft.au=Meng%20Yafeng&rft.date=2009-05&rft.volume=1&rft.spage=161&rft.epage=164&rft.pages=161-164&rft.isbn=9780769536002&rft.isbn_list=076953600X&rft_id=info:doi/10.1109/IFITA.2009.560&rft_dat=%3Cieee_6IE%3E5231552%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5231552&rfr_iscdi=true |