Fusion & Information Acquisition
There are multiple types of information which can be extracted from sensor actions and which affect the fusion of sensor data. Some information can be anticipated in the form of predicting which information will maximally reduce our uncertainty about a random variable, and some of it is after-the-fa...
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| creator | Hintz, K.J. |
| description | There are multiple types of information which can be extracted from sensor actions and which affect the fusion of sensor data. Some information can be anticipated in the form of predicting which information will maximally reduce our uncertainty about a random variable, and some of it is after-the-fact and can be used to change the quality of fusion by, for example, selecting different state estimator process models. The next level of improving fusion is by actively determining which information for a sensor system to obtain and therefore taking a proactive roll in the fusion process, rather than simply performing the best fusion of data that is provided |
| doi_str_mv | 10.1109/ICIF.2006.301788 |
| format | Conference Proceeding |
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Some information can be anticipated in the form of predicting which information will maximally reduce our uncertainty about a random variable, and some of it is after-the-fact and can be used to change the quality of fusion by, for example, selecting different state estimator process models. The next level of improving fusion is by actively determining which information for a sensor system to obtain and therefore taking a proactive roll in the fusion process, rather than simply performing the best fusion of data that is provided</description><identifier>ISBN: 1424409535</identifier><identifier>ISBN: 9781424409532</identifier><identifier>EISBN: 9780972184465</identifier><identifier>EISBN: 0972184465</identifier><identifier>DOI: 10.1109/ICIF.2006.301788</identifier><language>eng</language><publisher>IEEE</publisher><subject>Data mining ; Kinematics ; Predictive models ; Random variables ; resource allocation ; Resource management ; Sensor fusion ; sensor information ; Sensor systems ; situation information ; State estimation ; Target tracking ; Uncertainty</subject><ispartof>2006 9th International Conference on Information Fusion, 2006, p.1-2</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/4086074$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4086074$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hintz, K.J.</creatorcontrib><title>Fusion & Information Acquisition</title><title>2006 9th International Conference on Information Fusion</title><addtitle>ICIF</addtitle><description>There are multiple types of information which can be extracted from sensor actions and which affect the fusion of sensor data. Some information can be anticipated in the form of predicting which information will maximally reduce our uncertainty about a random variable, and some of it is after-the-fact and can be used to change the quality of fusion by, for example, selecting different state estimator process models. The next level of improving fusion is by actively determining which information for a sensor system to obtain and therefore taking a proactive roll in the fusion process, rather than simply performing the best fusion of data that is provided</description><subject>Data mining</subject><subject>Kinematics</subject><subject>Predictive models</subject><subject>Random variables</subject><subject>resource allocation</subject><subject>Resource management</subject><subject>Sensor fusion</subject><subject>sensor information</subject><subject>Sensor systems</subject><subject>situation information</subject><subject>State estimation</subject><subject>Target tracking</subject><subject>Uncertainty</subject><isbn>1424409535</isbn><isbn>9781424409532</isbn><isbn>9780972184465</isbn><isbn>0972184465</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjj1PwzAURV0hpELJjsSSiS3h2X7-eGMVNRCpEkv3yjHPkhFtIW4H_j2t6F3uucvRFeJRQisl0MvQDX2rAGyrQTrvZ6Ii54Gckh7RmhtxL1EhAhlt5qIq5RPO0WSk9Xei7k8lH_b1cz3s02HaheNlLePPKZd84Qdxm8JX4eraC7HpV5vurVm_vw7dct1kgmPj0EeKzhF5jH5kGIkdB1IWtQo8Gq0VwflvTCkQJh7VR-TgZIQQICW9EE__2szM2-8p78L0u0XwFhzqPwKAPo4</recordid><startdate>200607</startdate><enddate>200607</enddate><creator>Hintz, K.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200607</creationdate><title>Fusion & Information Acquisition</title><author>Hintz, K.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-748c9c779984c8be0b9e7ea926432aeb533290109cffa94feb2dcea71c0aa0ff3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Data mining</topic><topic>Kinematics</topic><topic>Predictive models</topic><topic>Random variables</topic><topic>resource allocation</topic><topic>Resource management</topic><topic>Sensor fusion</topic><topic>sensor information</topic><topic>Sensor systems</topic><topic>situation information</topic><topic>State estimation</topic><topic>Target tracking</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Hintz, K.J.</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 Electronic Library (IEL)</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>Hintz, K.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fusion & Information Acquisition</atitle><btitle>2006 9th International Conference on Information Fusion</btitle><stitle>ICIF</stitle><date>2006-07</date><risdate>2006</risdate><spage>1</spage><epage>2</epage><pages>1-2</pages><isbn>1424409535</isbn><isbn>9781424409532</isbn><eisbn>9780972184465</eisbn><eisbn>0972184465</eisbn><abstract>There are multiple types of information which can be extracted from sensor actions and which affect the fusion of sensor data. Some information can be anticipated in the form of predicting which information will maximally reduce our uncertainty about a random variable, and some of it is after-the-fact and can be used to change the quality of fusion by, for example, selecting different state estimator process models. The next level of improving fusion is by actively determining which information for a sensor system to obtain and therefore taking a proactive roll in the fusion process, rather than simply performing the best fusion of data that is provided</abstract><pub>IEEE</pub><doi>10.1109/ICIF.2006.301788</doi><tpages>2</tpages></addata></record> |
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| identifier | ISBN: 1424409535 |
| ispartof | 2006 9th International Conference on Information Fusion, 2006, p.1-2 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Data mining Kinematics Predictive models Random variables resource allocation Resource management Sensor fusion sensor information Sensor systems situation information State estimation Target tracking Uncertainty |
| title | Fusion & Information Acquisition |
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