Expert reasoning based improvements to the GLRT

This paper builds on the ideas presented in [1] and [2] to create a more robust Space-Time Adaptive Processing (STAP) system. Through the use of extensive knowledge bases, circular SAR registration, and expert reasoning, the system has been shown to increase the detection performance in non-homogene...

Full description

Saved in:
Bibliographic Details
Main Authors: Alfaysale, Nihad, Aljohani, Mansour, Burwell, Alex, Lin, Ethan, Wetzel, Daniel, Wicks, Michael C.
Format: Conference Proceeding
Language:English
Subjects:
Clutter
Cognition
Covariance matrices
Estimation
Synthetic aperture radar
Training data
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1194
container_issue
container_start_page 1189
container_title
container_volume
creator Alfaysale, Nihad
Aljohani, Mansour
Burwell, Alex
Lin, Ethan
Wetzel, Daniel
Wicks, Michael C.
description This paper builds on the ideas presented in [1] and [2] to create a more robust Space-Time Adaptive Processing (STAP) system. Through the use of extensive knowledge bases, circular SAR registration, and expert reasoning, the system has been shown to increase the detection performance in non-homogeneous environments.
doi_str_mv 10.1109/RADAR.2018.8378731
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8378731</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8378731</ieee_id><sourcerecordid>8378731</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-14ae7011d78e180b1b6a64ed8352a1b4b9464ebb84203c629d28aa7dd94c1323</originalsourceid><addsrcrecordid>eNotj81Kw0AUhUdBsLZ9Ad3MCySdOz-ZO8tQaxUCQtp9melcNWJ-yATRtzdgV4fzLQ7fYeweRA4g3KYuH8s6lwIwR2XRKrhia2cRjMJCQ2Hgmi2ksiYzCvCW3aX0KYRRM1qwze5noHHiI_nUd033zoNPFHnTDmP_TS11U-JTz6cP4vuqPq7YzZv_SrS-5JIdnnbH7XNWve5ftmWVNU5MGWhPVgBEiwQoAoTCF5oiKiM9BB2cnmsIqKVQ50K6KNF7G6PTZ5jFluzhf7UhotMwNq0ff0-Xd-oP4bxCWA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Expert reasoning based improvements to the GLRT</title><source>IEEE Xplore All Conference Series</source><creator>Alfaysale, Nihad ; Aljohani, Mansour ; Burwell, Alex ; Lin, Ethan ; Wetzel, Daniel ; Wicks, Michael C.</creator><creatorcontrib>Alfaysale, Nihad ; Aljohani, Mansour ; Burwell, Alex ; Lin, Ethan ; Wetzel, Daniel ; Wicks, Michael C.</creatorcontrib><description>This paper builds on the ideas presented in [1] and [2] to create a more robust Space-Time Adaptive Processing (STAP) system. Through the use of extensive knowledge bases, circular SAR registration, and expert reasoning, the system has been shown to increase the detection performance in non-homogeneous environments.</description><identifier>EISSN: 2375-5318</identifier><identifier>EISBN: 9781538641651</identifier><identifier>EISBN: 1538641658</identifier><identifier>EISBN: 1538641674</identifier><identifier>EISBN: 9781538641675</identifier><identifier>DOI: 10.1109/RADAR.2018.8378731</identifier><language>eng</language><publisher>IEEE</publisher><subject>Clutter ; Cognition ; Covariance matrices ; Estimation ; Synthetic aperture radar ; Training data</subject><ispartof>2018 IEEE Radar Conference (RadarConf18), 2018, p.1189-1194</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/8378731$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8378731$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Alfaysale, Nihad</creatorcontrib><creatorcontrib>Aljohani, Mansour</creatorcontrib><creatorcontrib>Burwell, Alex</creatorcontrib><creatorcontrib>Lin, Ethan</creatorcontrib><creatorcontrib>Wetzel, Daniel</creatorcontrib><creatorcontrib>Wicks, Michael C.</creatorcontrib><title>Expert reasoning based improvements to the GLRT</title><title>2018 IEEE Radar Conference (RadarConf18)</title><addtitle>RADAR</addtitle><description>This paper builds on the ideas presented in [1] and [2] to create a more robust Space-Time Adaptive Processing (STAP) system. Through the use of extensive knowledge bases, circular SAR registration, and expert reasoning, the system has been shown to increase the detection performance in non-homogeneous environments.</description><subject>Clutter</subject><subject>Cognition</subject><subject>Covariance matrices</subject><subject>Estimation</subject><subject>Synthetic aperture radar</subject><subject>Training data</subject><issn>2375-5318</issn><isbn>9781538641651</isbn><isbn>1538641658</isbn><isbn>1538641674</isbn><isbn>9781538641675</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81Kw0AUhUdBsLZ9Ad3MCySdOz-ZO8tQaxUCQtp9melcNWJ-yATRtzdgV4fzLQ7fYeweRA4g3KYuH8s6lwIwR2XRKrhia2cRjMJCQ2Hgmi2ksiYzCvCW3aX0KYRRM1qwze5noHHiI_nUd033zoNPFHnTDmP_TS11U-JTz6cP4vuqPq7YzZv_SrS-5JIdnnbH7XNWve5ftmWVNU5MGWhPVgBEiwQoAoTCF5oiKiM9BB2cnmsIqKVQ50K6KNF7G6PTZ5jFluzhf7UhotMwNq0ff0-Xd-oP4bxCWA</recordid><startdate>201804</startdate><enddate>201804</enddate><creator>Alfaysale, Nihad</creator><creator>Aljohani, Mansour</creator><creator>Burwell, Alex</creator><creator>Lin, Ethan</creator><creator>Wetzel, Daniel</creator><creator>Wicks, Michael C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201804</creationdate><title>Expert reasoning based improvements to the GLRT</title><author>Alfaysale, Nihad ; Aljohani, Mansour ; Burwell, Alex ; Lin, Ethan ; Wetzel, Daniel ; Wicks, Michael C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-14ae7011d78e180b1b6a64ed8352a1b4b9464ebb84203c629d28aa7dd94c1323</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Clutter</topic><topic>Cognition</topic><topic>Covariance matrices</topic><topic>Estimation</topic><topic>Synthetic aperture radar</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Alfaysale, Nihad</creatorcontrib><creatorcontrib>Aljohani, Mansour</creatorcontrib><creatorcontrib>Burwell, Alex</creatorcontrib><creatorcontrib>Lin, Ethan</creatorcontrib><creatorcontrib>Wetzel, Daniel</creatorcontrib><creatorcontrib>Wicks, Michael C.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Alfaysale, Nihad</au><au>Aljohani, Mansour</au><au>Burwell, Alex</au><au>Lin, Ethan</au><au>Wetzel, Daniel</au><au>Wicks, Michael C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Expert reasoning based improvements to the GLRT</atitle><btitle>2018 IEEE Radar Conference (RadarConf18)</btitle><stitle>RADAR</stitle><date>2018-04</date><risdate>2018</risdate><spage>1189</spage><epage>1194</epage><pages>1189-1194</pages><eissn>2375-5318</eissn><eisbn>9781538641651</eisbn><eisbn>1538641658</eisbn><eisbn>1538641674</eisbn><eisbn>9781538641675</eisbn><abstract>This paper builds on the ideas presented in [1] and [2] to create a more robust Space-Time Adaptive Processing (STAP) system. Through the use of extensive knowledge bases, circular SAR registration, and expert reasoning, the system has been shown to increase the detection performance in non-homogeneous environments.</abstract><pub>IEEE</pub><doi>10.1109/RADAR.2018.8378731</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2375-5318
ispartof 2018 IEEE Radar Conference (RadarConf18), 2018, p.1189-1194
issn 2375-5318
language eng
recordid cdi_ieee_primary_8378731
source IEEE Xplore All Conference Series
subjects Clutter
Cognition
Covariance matrices
Estimation
Synthetic aperture radar
Training data
title Expert reasoning based improvements to the GLRT
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T18%3A53%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Expert%20reasoning%20based%20improvements%20to%20the%20GLRT&rft.btitle=2018%20IEEE%20Radar%20Conference%20(RadarConf18)&rft.au=Alfaysale,%20Nihad&rft.date=2018-04&rft.spage=1189&rft.epage=1194&rft.pages=1189-1194&rft.eissn=2375-5318&rft_id=info:doi/10.1109/RADAR.2018.8378731&rft.eisbn=9781538641651&rft.eisbn_list=1538641658&rft.eisbn_list=1538641674&rft.eisbn_list=9781538641675&rft_dat=%3Cieee_CHZPO%3E8378731%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i90t-14ae7011d78e180b1b6a64ed8352a1b4b9464ebb84203c629d28aa7dd94c1323%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=8378731&rfr_iscdi=true