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Knowledge-Aided Adaptive Detection of Radar Target in Gaussian Clutter
This paper presents an adaptive detection method of radar target using the prior knowledge of Gaussian clutter. It is assumed that the clutter covariance matrix is random and obeys the inverse complex Wishart distribution. Based on the prior knowledge, we propose an adaptive detector via utilizing t...
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creator | Zhang, Xinyu Han, Jin-wang Zhang, Xinliang |
description | This paper presents an adaptive detection method of radar target using the prior knowledge of Gaussian clutter. It is assumed that the clutter covariance matrix is random and obeys the inverse complex Wishart distribution. Based on the prior knowledge, we propose an adaptive detector via utilizing the generalized likelihood ratio test. The proposed adaptive detector does not need training data and detection performance can be achieved due to the use of a priori knowledge about the clutter. Finally, the detection performance of the proposed detector is evaluated and the results illustrate that the proposed detector is superior to the conventional counterparts, particularly for small sample number of the received signal. |
doi_str_mv | 10.1109/SAM48682.2020.9104294 |
format | conference_proceeding |
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It is assumed that the clutter covariance matrix is random and obeys the inverse complex Wishart distribution. Based on the prior knowledge, we propose an adaptive detector via utilizing the generalized likelihood ratio test. The proposed adaptive detector does not need training data and detection performance can be achieved due to the use of a priori knowledge about the clutter. Finally, the detection performance of the proposed detector is evaluated and the results illustrate that the proposed detector is superior to the conventional counterparts, particularly for small sample number of the received signal.</description><identifier>EISSN: 2151-870X</identifier><identifier>EISBN: 1728119464</identifier><identifier>EISBN: 9781728119465</identifier><identifier>DOI: 10.1109/SAM48682.2020.9104294</identifier><language>eng</language><publisher>IEEE</publisher><subject>Array signal processing ; Conferences ; Detectors ; Gaussian clutter ; generalized likelihood ratio test ; MIMO radar ; prior knowledge ; Radar clutter ; Radar detection ; Target detection ; Training data</subject><ispartof>2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2020, p.1-5</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/9104294$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,23909,23910,25118,27902,54530,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9104294$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhang, Xinyu</creatorcontrib><creatorcontrib>Han, Jin-wang</creatorcontrib><creatorcontrib>Zhang, Xinliang</creatorcontrib><title>Knowledge-Aided Adaptive Detection of Radar Target in Gaussian Clutter</title><title>2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)</title><addtitle>SAM</addtitle><description>This paper presents an adaptive detection method of radar target using the prior knowledge of Gaussian clutter. It is assumed that the clutter covariance matrix is random and obeys the inverse complex Wishart distribution. Based on the prior knowledge, we propose an adaptive detector via utilizing the generalized likelihood ratio test. The proposed adaptive detector does not need training data and detection performance can be achieved due to the use of a priori knowledge about the clutter. Finally, the detection performance of the proposed detector is evaluated and the results illustrate that the proposed detector is superior to the conventional counterparts, particularly for small sample number of the received signal.</description><subject>Array signal processing</subject><subject>Conferences</subject><subject>Detectors</subject><subject>Gaussian clutter</subject><subject>generalized likelihood ratio test</subject><subject>MIMO radar</subject><subject>prior knowledge</subject><subject>Radar clutter</subject><subject>Radar detection</subject><subject>Target detection</subject><subject>Training data</subject><issn>2151-870X</issn><isbn>1728119464</isbn><isbn>9781728119465</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj91KwzAYQKMguE2fQIS8QOv35adNLkt1U5wI2gvvRtJ8HZHZjjZTfPsJ7urcHc5h7BYhRwR79169KFMYkQsQkFsEJaw6Y3MshUG0qlDnbCZQY2ZK-Lhk82n6BFAlSD1jy-d--NlR2FJWxUCBV8HtU_wmfk-J2hSHng8df3PBjbxx45YSjz1fucM0RdfzendIicYrdtG53UTXJy5Ys3xo6sds_bp6qqt1FgXIlP1VEgqnvfFkBJSgdMDWSwnGUteGrtRaQyDtvSvQtUBSo1DBSG9LArlgN__aSESb_Ri_3Pi7OS3LI2HbSu8</recordid><startdate>202006</startdate><enddate>202006</enddate><creator>Zhang, Xinyu</creator><creator>Han, Jin-wang</creator><creator>Zhang, Xinliang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>202006</creationdate><title>Knowledge-Aided Adaptive Detection of Radar Target in Gaussian Clutter</title><author>Zhang, Xinyu ; Han, Jin-wang ; Zhang, Xinliang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-682e12a5b8be8207045d1cb33089efcdf75550de5bba61ac0e35124d83b97e03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Array signal processing</topic><topic>Conferences</topic><topic>Detectors</topic><topic>Gaussian clutter</topic><topic>generalized likelihood ratio test</topic><topic>MIMO radar</topic><topic>prior knowledge</topic><topic>Radar clutter</topic><topic>Radar detection</topic><topic>Target detection</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xinyu</creatorcontrib><creatorcontrib>Han, Jin-wang</creatorcontrib><creatorcontrib>Zhang, Xinliang</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>Zhang, Xinyu</au><au>Han, Jin-wang</au><au>Zhang, Xinliang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Knowledge-Aided Adaptive Detection of Radar Target in Gaussian Clutter</atitle><btitle>2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)</btitle><stitle>SAM</stitle><date>2020-06</date><risdate>2020</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eissn>2151-870X</eissn><eisbn>1728119464</eisbn><eisbn>9781728119465</eisbn><abstract>This paper presents an adaptive detection method of radar target using the prior knowledge of Gaussian clutter. It is assumed that the clutter covariance matrix is random and obeys the inverse complex Wishart distribution. Based on the prior knowledge, we propose an adaptive detector via utilizing the generalized likelihood ratio test. The proposed adaptive detector does not need training data and detection performance can be achieved due to the use of a priori knowledge about the clutter. Finally, the detection performance of the proposed detector is evaluated and the results illustrate that the proposed detector is superior to the conventional counterparts, particularly for small sample number of the received signal.</abstract><pub>IEEE</pub><doi>10.1109/SAM48682.2020.9104294</doi><tpages>5</tpages></addata></record> |
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identifier | EISSN: 2151-870X |
ispartof | 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2020, p.1-5 |
issn | 2151-870X |
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source | IEEE Xplore All Conference Series |
subjects | Array signal processing Conferences Detectors Gaussian clutter generalized likelihood ratio test MIMO radar prior knowledge Radar clutter Radar detection Target detection Training data |
title | Knowledge-Aided Adaptive Detection of Radar Target in Gaussian Clutter |
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