Multiple hypothesis tracking using clustered measurements
This paper introduces an algorithm for tracking targets whose locations are inferred from clusters of observations. This method, which we call MHTC, expands the traditional multiple hypothesis tracking (MHT) hypothesis tree to include model hypotheses - possible ways the data can be clustered in eac...
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| creator | Wolf, M.T. Burdick, J.W. |
| description | This paper introduces an algorithm for tracking targets whose locations are inferred from clusters of observations. This method, which we call MHTC, expands the traditional multiple hypothesis tracking (MHT) hypothesis tree to include model hypotheses - possible ways the data can be clustered in each time step - as well as ways the measurements can be associated with existing targets across time steps. We present this new hypothesis framework and its probability expressions and demonstrate MHTC's operation in a robotic solution to tracking neural signal sources. |
| doi_str_mv | 10.1109/ROBOT.2009.5152841 |
| format | Conference Proceeding |
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We present this new hypothesis framework and its probability expressions and demonstrate MHTC's operation in a robotic solution to tracking neural signal sources.</description><identifier>ISSN: 1050-4729</identifier><identifier>ISBN: 1424427886</identifier><identifier>ISBN: 9781424427888</identifier><identifier>EISSN: 2577-087X</identifier><identifier>EISBN: 1424427894</identifier><identifier>EISBN: 9781424427895</identifier><identifier>DOI: 10.1109/ROBOT.2009.5152841</identifier><identifier>LCCN: 90-640158</identifier><language>eng</language><publisher>IEEE</publisher><subject>Electrodes ; Mechanical variables measurement ; Neurons ; Paper technology ; Propulsion ; Radar tracking ; Robot sensing systems ; Robotics and automation ; State estimation ; Target tracking</subject><ispartof>2009 IEEE International Conference on Robotics and Automation, 2009, p.3955-3961</ispartof><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://ieeexplore.ieee.org/document/5152841$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5152841$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wolf, M.T.</creatorcontrib><creatorcontrib>Burdick, J.W.</creatorcontrib><title>Multiple hypothesis tracking using clustered measurements</title><title>2009 IEEE International Conference on Robotics and Automation</title><addtitle>ROBOT</addtitle><description>This paper introduces an algorithm for tracking targets whose locations are inferred from clusters of observations. This method, which we call MHTC, expands the traditional multiple hypothesis tracking (MHT) hypothesis tree to include model hypotheses - possible ways the data can be clustered in each time step - as well as ways the measurements can be associated with existing targets across time steps. We present this new hypothesis framework and its probability expressions and demonstrate MHTC's operation in a robotic solution to tracking neural signal sources.</description><subject>Electrodes</subject><subject>Mechanical variables measurement</subject><subject>Neurons</subject><subject>Paper technology</subject><subject>Propulsion</subject><subject>Radar tracking</subject><subject>Robot sensing systems</subject><subject>Robotics and automation</subject><subject>State estimation</subject><subject>Target tracking</subject><issn>1050-4729</issn><issn>2577-087X</issn><isbn>1424427886</isbn><isbn>9781424427888</isbn><isbn>1424427894</isbn><isbn>9781424427895</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkMtKw0AUhsdLwVj7ArrJCySeM_cstdQLVAJSwV2ZyZzYaFJDJln07UUsuPm_xQff4mfsGiFHhOL2tbwvNzkHKHKFiluJJ-wSJZeSG1vIU5ZwZUwG1ryf_Qurz1mCoCCThhczlhSQaQmo7AVbxPgJAGi0FCgSVrxM7dj0LaW7Q_897ig2MR0HV301-490ir9btVMcaaCQduTiNFBH-zFesVnt2kiLI-fs7WG1WT5l6_LxeXm3zhoUEjMfyJhQg_dWk68sQaXR-GCMESACcS2tCtpxDr4m4XggrMA6VVcehLRizm7-ug0Rbfuh6dxw2B7vED9z2U7i</recordid><startdate>200905</startdate><enddate>200905</enddate><creator>Wolf, M.T.</creator><creator>Burdick, J.W.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200905</creationdate><title>Multiple hypothesis tracking using clustered measurements</title><author>Wolf, M.T. ; Burdick, J.W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1341-bde77df0bb86ebc8e0c617bd777303de26485d6a220bfe3a2de1c08a5fcb03483</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Electrodes</topic><topic>Mechanical variables measurement</topic><topic>Neurons</topic><topic>Paper technology</topic><topic>Propulsion</topic><topic>Radar tracking</topic><topic>Robot sensing systems</topic><topic>Robotics and automation</topic><topic>State estimation</topic><topic>Target tracking</topic><toplevel>online_resources</toplevel><creatorcontrib>Wolf, M.T.</creatorcontrib><creatorcontrib>Burdick, J.W.</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 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>Wolf, M.T.</au><au>Burdick, J.W.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multiple hypothesis tracking using clustered measurements</atitle><btitle>2009 IEEE International Conference on Robotics and Automation</btitle><stitle>ROBOT</stitle><date>2009-05</date><risdate>2009</risdate><spage>3955</spage><epage>3961</epage><pages>3955-3961</pages><issn>1050-4729</issn><eissn>2577-087X</eissn><isbn>1424427886</isbn><isbn>9781424427888</isbn><eisbn>1424427894</eisbn><eisbn>9781424427895</eisbn><abstract>This paper introduces an algorithm for tracking targets whose locations are inferred from clusters of observations. This method, which we call MHTC, expands the traditional multiple hypothesis tracking (MHT) hypothesis tree to include model hypotheses - possible ways the data can be clustered in each time step - as well as ways the measurements can be associated with existing targets across time steps. We present this new hypothesis framework and its probability expressions and demonstrate MHTC's operation in a robotic solution to tracking neural signal sources.</abstract><pub>IEEE</pub><doi>10.1109/ROBOT.2009.5152841</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1050-4729 |
| ispartof | 2009 IEEE International Conference on Robotics and Automation, 2009, p.3955-3961 |
| issn | 1050-4729 2577-087X |
| language | eng |
| recordid | cdi_ieee_primary_5152841 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Electrodes Mechanical variables measurement Neurons Paper technology Propulsion Radar tracking Robot sensing systems Robotics and automation State estimation Target tracking |
| title | Multiple hypothesis tracking using clustered measurements |
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