A Robust Matched Detector
We address the matched detector problem in the case the signal to be detected is imperfectly known. While in the standard detector the signal is known to lie along a particular direction, we consider the case where this direction is known up to additive white Gaussian noise. This somehow amounts to...
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| Veröffentlicht in: | IEEE transactions on signal processing 2007-11, Vol.55 (11), p.5133-5142 |
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| container_title | IEEE transactions on signal processing |
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| creator | Fuchs, J.J. |
| description | We address the matched detector problem in the case the signal to be detected is imperfectly known. While in the standard detector the signal is known to lie along a particular direction, we consider the case where this direction is known up to additive white Gaussian noise. This somehow amounts to assuming that the signal lies in a cone the aperture of which depends upon the level of uncertainty. We build the associated generalized likelihood ratio (GLR), analyze its statistical properties, indicate how to set the threshold to achieve a given false alarm rate, and how to predict the associated probability of detection. The so-obtained detector reduces to the conventional one when the uncertainty vanishes and we analyze its behavior when the level of uncertainty, which has to be known a priori, is mis-evaluated. It appears that the sensitivity of the detector is quite low with respect to this kind of errors. More importantly several realistic examples are presented that indicate that the proposed detector remains quite efficient when the true signals are far from being of the assumed model and whatever the model of the uncertainty actually is. It is this robustness that makes the detector valuable. |
| doi_str_mv | 10.1109/TSP.2007.898786 |
| format | Article |
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While in the standard detector the signal is known to lie along a particular direction, we consider the case where this direction is known up to additive white Gaussian noise. This somehow amounts to assuming that the signal lies in a cone the aperture of which depends upon the level of uncertainty. We build the associated generalized likelihood ratio (GLR), analyze its statistical properties, indicate how to set the threshold to achieve a given false alarm rate, and how to predict the associated probability of detection. The so-obtained detector reduces to the conventional one when the uncertainty vanishes and we analyze its behavior when the level of uncertainty, which has to be known a priori, is mis-evaluated. It appears that the sensitivity of the detector is quite low with respect to this kind of errors. More importantly several realistic examples are presented that indicate that the proposed detector remains quite efficient when the true signals are far from being of the assumed model and whatever the model of the uncertainty actually is. 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(IEEE) 2007</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c458t-82df884f3a163916c9cafae75ce9a398f14a7c8bb9277014c764f45ce4ecb9e23</citedby><cites>FETCH-LOGICAL-c458t-82df884f3a163916c9cafae75ce9a398f14a7c8bb9277014c764f45ce4ecb9e23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4352123$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,777,781,793,882,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4352123$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19186239$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://inria.hal.science/inria-00504235$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Fuchs, J.J.</creatorcontrib><title>A Robust Matched Detector</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>We address the matched detector problem in the case the signal to be detected is imperfectly known. While in the standard detector the signal is known to lie along a particular direction, we consider the case where this direction is known up to additive white Gaussian noise. This somehow amounts to assuming that the signal lies in a cone the aperture of which depends upon the level of uncertainty. We build the associated generalized likelihood ratio (GLR), analyze its statistical properties, indicate how to set the threshold to achieve a given false alarm rate, and how to predict the associated probability of detection. The so-obtained detector reduces to the conventional one when the uncertainty vanishes and we analyze its behavior when the level of uncertainty, which has to be known a priori, is mis-evaluated. It appears that the sensitivity of the detector is quite low with respect to this kind of errors. More importantly several realistic examples are presented that indicate that the proposed detector remains quite efficient when the true signals are far from being of the assumed model and whatever the model of the uncertainty actually is. It is this robustness that makes the detector valuable.</description><subject>Additive noise</subject><subject>Additive white noise</subject><subject>Apertures</subject><subject>Applied sciences</subject><subject>Computer Science</subject><subject>Construction</subject><subject>Detection</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Detectors</subject><subject>Engineering Sciences</subject><subject>Exact sciences and technology</subject><subject>False alarms</subject><subject>Gaussian</subject><subject>generalized likelihood ratio test (GLRT)</subject><subject>hypothesis testing</subject><subject>Image Processing</subject><subject>Information, signal and communications theory</subject><subject>Interference</subject><subject>Likelihood ratio</subject><subject>matched filter</subject><subject>Matched filters</subject><subject>Mathematical models</subject><subject>Noise robustness</subject><subject>Robustness</subject><subject>Signal and communications theory</subject><subject>Signal and Image Processing</subject><subject>Signal detection</subject><subject>Signal, noise</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><subject>Testing</subject><subject>total least squares</subject><subject>Uncertainty</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kMtLAzEQxoMoWKtnES9FUA-ybd6PY6mPChVFK3gL2TShW7bdmuwK_vembKngwcMwA_Ob-WY-AE4R7CME1WD69tLHEIq-VFJIvgc6SFGUQSr4fqohIxmT4uMQHMW4gBBRqngHnA17r1XexLr3ZGo7d7PeraudratwDA68KaM72eYueL-_m47G2eT54XE0nGSWMllnEs-8lNQTgzhRiFtljTdOMOuUIUp6RI2wMs8VFiKpWsGpp6lLnc2Vw6QLbtq9c1PqdSiWJnzryhR6PJzoYhUKoyFkkGLCvlCir1t6HarPxsVaL4toXVmalauaqKVURKSgibz6lyRUIawkT-DFH3BRNWGVftaSU0Y5ZBvdQQvZUMUYnN-diqDe-K-T_3rjv279TxOX27UmWlP6YFa2iL9jCkmOiUrcecsVzrldmxKGESbkB7wDikE</recordid><startdate>20071101</startdate><enddate>20071101</enddate><creator>Fuchs, J.J.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope><scope>1XC</scope></search><sort><creationdate>20071101</creationdate><title>A Robust Matched Detector</title><author>Fuchs, J.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c458t-82df884f3a163916c9cafae75ce9a398f14a7c8bb9277014c764f45ce4ecb9e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Additive noise</topic><topic>Additive white noise</topic><topic>Apertures</topic><topic>Applied sciences</topic><topic>Computer Science</topic><topic>Construction</topic><topic>Detection</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Detectors</topic><topic>Engineering Sciences</topic><topic>Exact sciences and technology</topic><topic>False alarms</topic><topic>Gaussian</topic><topic>generalized likelihood ratio test (GLRT)</topic><topic>hypothesis testing</topic><topic>Image Processing</topic><topic>Information, signal and communications theory</topic><topic>Interference</topic><topic>Likelihood ratio</topic><topic>matched filter</topic><topic>Matched filters</topic><topic>Mathematical models</topic><topic>Noise robustness</topic><topic>Robustness</topic><topic>Signal and communications theory</topic><topic>Signal and Image Processing</topic><topic>Signal detection</topic><topic>Signal, noise</topic><topic>Studies</topic><topic>Telecommunications and information theory</topic><topic>Testing</topic><topic>total least squares</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fuchs, J.J.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fuchs, J.J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Robust Matched Detector</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2007-11-01</date><risdate>2007</risdate><volume>55</volume><issue>11</issue><spage>5133</spage><epage>5142</epage><pages>5133-5142</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>We address the matched detector problem in the case the signal to be detected is imperfectly known. While in the standard detector the signal is known to lie along a particular direction, we consider the case where this direction is known up to additive white Gaussian noise. This somehow amounts to assuming that the signal lies in a cone the aperture of which depends upon the level of uncertainty. We build the associated generalized likelihood ratio (GLR), analyze its statistical properties, indicate how to set the threshold to achieve a given false alarm rate, and how to predict the associated probability of detection. The so-obtained detector reduces to the conventional one when the uncertainty vanishes and we analyze its behavior when the level of uncertainty, which has to be known a priori, is mis-evaluated. It appears that the sensitivity of the detector is quite low with respect to this kind of errors. More importantly several realistic examples are presented that indicate that the proposed detector remains quite efficient when the true signals are far from being of the assumed model and whatever the model of the uncertainty actually is. It is this robustness that makes the detector valuable.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2007.898786</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| subjects | Additive noise Additive white noise Apertures Applied sciences Computer Science Construction Detection Detection, estimation, filtering, equalization, prediction Detectors Engineering Sciences Exact sciences and technology False alarms Gaussian generalized likelihood ratio test (GLRT) hypothesis testing Image Processing Information, signal and communications theory Interference Likelihood ratio matched filter Matched filters Mathematical models Noise robustness Robustness Signal and communications theory Signal and Image Processing Signal detection Signal, noise Studies Telecommunications and information theory Testing total least squares Uncertainty |
| title | A Robust Matched Detector |
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