A unified approach to state estimation problems under data and model uncertainties
We present a unified approach to the problem of state estimation under measurement and model uncertainties. The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix c...
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| creator | Sigalov, D. Michaeli, T. Oshman, Y. |
| description | We present a unified approach to the problem of state estimation under measurement and model uncertainties. The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix coefficients. Consequently, all may be solved efficiently using a single IMM algorithm or using a linear optimal filter, derived elsewhere, thus replacing the need for deriving a unique algorithm for each problem. |
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The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix coefficients. Consequently, all may be solved efficiently using a single IMM algorithm or using a linear optimal filter, derived elsewhere, thus replacing the need for deriving a unique algorithm for each problem.</description><identifier>ISBN: 1467304174</identifier><identifier>ISBN: 9781467304177</identifier><identifier>EISBN: 0982443846</identifier><identifier>EISBN: 9780982443842</identifier><identifier>EISBN: 0982443854</identifier><identifier>EISBN: 9780982443859</identifier><language>eng</language><publisher>IEEE</publisher><subject>Clutter ; clutter and data association ; Covariance matrix ; hybrid systems ; Maneuvering target tracking ; Mathematical model ; multiple target tracking ; Noise ; Noise measurement ; Target tracking ; Time measurement</subject><ispartof>2012 15th International Conference on Information Fusion, 2012, p.2569-2576</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/6290466$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6290466$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Sigalov, D.</creatorcontrib><creatorcontrib>Michaeli, T.</creatorcontrib><creatorcontrib>Oshman, Y.</creatorcontrib><title>A unified approach to state estimation problems under data and model uncertainties</title><title>2012 15th International Conference on Information Fusion</title><addtitle>ICIF</addtitle><description>We present a unified approach to the problem of state estimation under measurement and model uncertainties. The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix coefficients. Consequently, all may be solved efficiently using a single IMM algorithm or using a linear optimal filter, derived elsewhere, thus replacing the need for deriving a unique algorithm for each problem.</description><subject>Clutter</subject><subject>clutter and data association</subject><subject>Covariance matrix</subject><subject>hybrid systems</subject><subject>Maneuvering target tracking</subject><subject>Mathematical model</subject><subject>multiple target tracking</subject><subject>Noise</subject><subject>Noise measurement</subject><subject>Target tracking</subject><subject>Time measurement</subject><isbn>1467304174</isbn><isbn>9781467304177</isbn><isbn>0982443846</isbn><isbn>9780982443842</isbn><isbn>0982443854</isbn><isbn>9780982443859</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9jLEKwjAURSMiaLVf4PJ-QEjakLSjiOIs7uXZvGKkTUoSB__eDM7e5cA9l7tgBW-bSsq6kWrJCiGVrrkUWq5ZGeOL5-hGCKU37HaEt7ODJQM4z8Fj_4TkISZMBBSTnTBZ7yCrx0hTzGtDAQwmBHQGJm9ozGVPIaF1yVLcsdWAY6Tyxy3bX8730_VgiaibQ74Mn05VLZdK1f_tF9XbPI0</recordid><startdate>201207</startdate><enddate>201207</enddate><creator>Sigalov, D.</creator><creator>Michaeli, T.</creator><creator>Oshman, Y.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201207</creationdate><title>A unified approach to state estimation problems under data and model uncertainties</title><author>Sigalov, D. ; Michaeli, T. ; Oshman, Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_62904663</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Clutter</topic><topic>clutter and data association</topic><topic>Covariance matrix</topic><topic>hybrid systems</topic><topic>Maneuvering target tracking</topic><topic>Mathematical model</topic><topic>multiple target tracking</topic><topic>Noise</topic><topic>Noise measurement</topic><topic>Target tracking</topic><topic>Time measurement</topic><toplevel>online_resources</toplevel><creatorcontrib>Sigalov, D.</creatorcontrib><creatorcontrib>Michaeli, T.</creatorcontrib><creatorcontrib>Oshman, Y.</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>Sigalov, D.</au><au>Michaeli, T.</au><au>Oshman, Y.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A unified approach to state estimation problems under data and model uncertainties</atitle><btitle>2012 15th International Conference on Information Fusion</btitle><stitle>ICIF</stitle><date>2012-07</date><risdate>2012</risdate><spage>2569</spage><epage>2576</epage><pages>2569-2576</pages><isbn>1467304174</isbn><isbn>9781467304177</isbn><eisbn>0982443846</eisbn><eisbn>9780982443842</eisbn><eisbn>0982443854</eisbn><eisbn>9780982443859</eisbn><abstract>We present a unified approach to the problem of state estimation under measurement and model uncertainties. The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix coefficients. Consequently, all may be solved efficiently using a single IMM algorithm or using a linear optimal filter, derived elsewhere, thus replacing the need for deriving a unique algorithm for each problem.</abstract><pub>IEEE</pub></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Clutter clutter and data association Covariance matrix hybrid systems Maneuvering target tracking Mathematical model multiple target tracking Noise Noise measurement Target tracking Time measurement |
| title | A unified approach to state estimation problems under data and model uncertainties |
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