DATA FUSION ANALYSIS FOR MARITIME AUTOMATIC TARGET RECOGNITION
A system and method for performing Automatic Target Recognition by combining the outputs of several classifiers. In one embodiment, feature vectors are extracted from radar images and fed to three classifiers. The classifiers include a Gaussian mixture model neural network, a radial basis function n...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Patent |
Sprache: | eng ; fre ; ger |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | STEPHAN, Bryan D SATHYENDRA, Harsha Modur |
description | A system and method for performing Automatic Target Recognition by combining the outputs of several classifiers. In one embodiment, feature vectors are extracted from radar images and fed to three classifiers. The classifiers include a Gaussian mixture model neural network, a radial basis function neural network, and a vector quantization classifier. The class designations generated by the classifiers are combined in a weighted voting system, i.e., the mode of the weighted classification decisions is selected as the overall class designation of the target. A confidence metric may be formed from the extent to which the class designations of the several classifiers are the same. This system is also designed to handle unknown target types and subsequent re-integration at a later time, effectively, artificially and automatically increasing the training database size. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_EP3152707A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>EP3152707A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_EP3152707A13</originalsourceid><addsrcrecordid>eNrjZLBzcQxxVHALDfb091Nw9HP0iQz2DFZw8w9S8HUM8gzx9HVVcAwN8fd1DPF0VghxDHJ3DVEIcnX2d_cDSvr78TCwpiXmFKfyQmluBgU31xBnD93Ugvz41OKCxOTUvNSSeNcAY0NTI3MDc0dDYyKUAAAVrSnf</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>DATA FUSION ANALYSIS FOR MARITIME AUTOMATIC TARGET RECOGNITION</title><source>esp@cenet</source><creator>STEPHAN, Bryan D ; SATHYENDRA, Harsha Modur</creator><creatorcontrib>STEPHAN, Bryan D ; SATHYENDRA, Harsha Modur</creatorcontrib><description>A system and method for performing Automatic Target Recognition by combining the outputs of several classifiers. In one embodiment, feature vectors are extracted from radar images and fed to three classifiers. The classifiers include a Gaussian mixture model neural network, a radial basis function neural network, and a vector quantization classifier. The class designations generated by the classifiers are combined in a weighted voting system, i.e., the mode of the weighted classification decisions is selected as the overall class designation of the target. A confidence metric may be formed from the extent to which the class designations of the several classifiers are the same. This system is also designed to handle unknown target types and subsequent re-integration at a later time, effectively, artificially and automatically increasing the training database size.</description><language>eng ; fre ; ger</language><subject>CALCULATING ; COMPUTING ; COUNTING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2017</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20170412&DB=EPODOC&CC=EP&NR=3152707A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20170412&DB=EPODOC&CC=EP&NR=3152707A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>STEPHAN, Bryan D</creatorcontrib><creatorcontrib>SATHYENDRA, Harsha Modur</creatorcontrib><title>DATA FUSION ANALYSIS FOR MARITIME AUTOMATIC TARGET RECOGNITION</title><description>A system and method for performing Automatic Target Recognition by combining the outputs of several classifiers. In one embodiment, feature vectors are extracted from radar images and fed to three classifiers. The classifiers include a Gaussian mixture model neural network, a radial basis function neural network, and a vector quantization classifier. The class designations generated by the classifiers are combined in a weighted voting system, i.e., the mode of the weighted classification decisions is selected as the overall class designation of the target. A confidence metric may be formed from the extent to which the class designations of the several classifiers are the same. This system is also designed to handle unknown target types and subsequent re-integration at a later time, effectively, artificially and automatically increasing the training database size.</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2017</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLBzcQxxVHALDfb091Nw9HP0iQz2DFZw8w9S8HUM8gzx9HVVcAwN8fd1DPF0VghxDHJ3DVEIcnX2d_cDSvr78TCwpiXmFKfyQmluBgU31xBnD93Ugvz41OKCxOTUvNSSeNcAY0NTI3MDc0dDYyKUAAAVrSnf</recordid><startdate>20170412</startdate><enddate>20170412</enddate><creator>STEPHAN, Bryan D</creator><creator>SATHYENDRA, Harsha Modur</creator><scope>EVB</scope></search><sort><creationdate>20170412</creationdate><title>DATA FUSION ANALYSIS FOR MARITIME AUTOMATIC TARGET RECOGNITION</title><author>STEPHAN, Bryan D ; SATHYENDRA, Harsha Modur</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP3152707A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2017</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>STEPHAN, Bryan D</creatorcontrib><creatorcontrib>SATHYENDRA, Harsha Modur</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>STEPHAN, Bryan D</au><au>SATHYENDRA, Harsha Modur</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>DATA FUSION ANALYSIS FOR MARITIME AUTOMATIC TARGET RECOGNITION</title><date>2017-04-12</date><risdate>2017</risdate><abstract>A system and method for performing Automatic Target Recognition by combining the outputs of several classifiers. In one embodiment, feature vectors are extracted from radar images and fed to three classifiers. The classifiers include a Gaussian mixture model neural network, a radial basis function neural network, and a vector quantization classifier. The class designations generated by the classifiers are combined in a weighted voting system, i.e., the mode of the weighted classification decisions is selected as the overall class designation of the target. A confidence metric may be formed from the extent to which the class designations of the several classifiers are the same. This system is also designed to handle unknown target types and subsequent re-integration at a later time, effectively, artificially and automatically increasing the training database size.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng ; fre ; ger |
recordid | cdi_epo_espacenet_EP3152707A1 |
source | esp@cenet |
subjects | CALCULATING COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
title | DATA FUSION ANALYSIS FOR MARITIME AUTOMATIC TARGET RECOGNITION |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T17%3A27%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=STEPHAN,%20Bryan%20D&rft.date=2017-04-12&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EEP3152707A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |