CFAR Knowledge-Aided Radar Detection With Heterogeneous Samples
In this letter, we consider the problem of constant false alarm rate (CFAR) detection in heterogeneous scenarios. We assume that the covariance matrices of the training samples are different from that of the cell under test and employ prior statistical knowledge to describe the degree of heterogenei...
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| Veröffentlicht in: | IEEE signal processing letters 2017-05, Vol.24 (5), p.693-697 |
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| creator | Wang, Yikai Xia, Wei He, Zishu |
| description | In this letter, we consider the problem of constant false alarm rate (CFAR) detection in heterogeneous scenarios. We assume that the covariance matrices of the training samples are different from that of the cell under test and employ prior statistical knowledge to describe the degree of heterogeneity. We derive an approximate expression of the average signal-to-clutter-noise ratio loss of the adaptive detector constructed with the maximum likelihood estimate of the covariance matrix obtained by solving the fixed-point equation. We validate the CFAR property of the detector and derive the asymptotic expression of the probability of false alarm. Exploiting such prior knowledge can significantly reduce the adverse effects of the heterogeneous training samples. Simulations validate the proposed theoretical results. |
| doi_str_mv | 10.1109/LSP.2017.2688386 |
| format | Article |
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We assume that the covariance matrices of the training samples are different from that of the cell under test and employ prior statistical knowledge to describe the degree of heterogeneity. We derive an approximate expression of the average signal-to-clutter-noise ratio loss of the adaptive detector constructed with the maximum likelihood estimate of the covariance matrix obtained by solving the fixed-point equation. We validate the CFAR property of the detector and derive the asymptotic expression of the probability of false alarm. Exploiting such prior knowledge can significantly reduce the adverse effects of the heterogeneous training samples. Simulations validate the proposed theoretical results.</description><subject>Asymptotic performance</subject><subject>Clutter</subject><subject>constant false alarm rate (CFAR) detection</subject><subject>Covariance matrices</subject><subject>Detectors</subject><subject>heterogeneous clutter</subject><subject>knowledge-aided radar detection</subject><subject>Mathematical model</subject><subject>Maximum likelihood estimation</subject><subject>Radar detection</subject><subject>Training</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEFLw0AUhBdRsFbvgpf8gdT3drO7LycJ1VoxoLSKx7DJvtRI25RsRPz3prR4mhmYmcMnxDXCBBHS23z5OpGAdiINkSJzIkaoNcVSGTwdPFiI0xToXFyE8AUAhKRH4m46yxbR87b9WbNfcZw1nn20cN510T33XPVNu40-mv4zmg-xa1e85fY7REu32a05XIqz2q0DXx11LN5nD2_TeZy_PD5NszyupFF9rLhKvDdQSkyUTrQ3dWlLtGlaGoOASiJjZSSQ9uCsM0qmZVImhAloqViNBRx-q64NoeO62HXNxnW_BUKxB1AMAIo9gOIIYJjcHCYNM__XLRFpIPUHGj1VCw</recordid><startdate>201705</startdate><enddate>201705</enddate><creator>Wang, Yikai</creator><creator>Xia, Wei</creator><creator>He, Zishu</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-6366-5080</orcidid></search><sort><creationdate>201705</creationdate><title>CFAR Knowledge-Aided Radar Detection With Heterogeneous Samples</title><author>Wang, Yikai ; Xia, Wei ; He, Zishu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c263t-3ec4dd60b2143545d6fb7b1799b66101321e1c62085d0a7a6329b4b48140523e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Asymptotic performance</topic><topic>Clutter</topic><topic>constant false alarm rate (CFAR) detection</topic><topic>Covariance matrices</topic><topic>Detectors</topic><topic>heterogeneous clutter</topic><topic>knowledge-aided radar detection</topic><topic>Mathematical model</topic><topic>Maximum likelihood estimation</topic><topic>Radar detection</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yikai</creatorcontrib><creatorcontrib>Xia, Wei</creatorcontrib><creatorcontrib>He, Zishu</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE</collection><collection>CrossRef</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Yikai</au><au>Xia, Wei</au><au>He, Zishu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CFAR Knowledge-Aided Radar Detection With Heterogeneous Samples</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2017-05</date><risdate>2017</risdate><volume>24</volume><issue>5</issue><spage>693</spage><epage>697</epage><pages>693-697</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>In this letter, we consider the problem of constant false alarm rate (CFAR) detection in heterogeneous scenarios. 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| subjects | Asymptotic performance Clutter constant false alarm rate (CFAR) detection Covariance matrices Detectors heterogeneous clutter knowledge-aided radar detection Mathematical model Maximum likelihood estimation Radar detection Training |
| title | CFAR Knowledge-Aided Radar Detection With Heterogeneous Samples |
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