Object Tracking Algorithm Based on Grey Innovation Model GM (1, 1) of Fixed Length
An algorithm based on grey innovation model GM (1, 1) of fixed length is introduced for the localization and tracking of moving targets. Kalman filter is an efficient computational method for tracking, but motion and noise assumption limits its process model to constant velocity model or constant ac...
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| creator | Fu Qiang Xiao Yunshi Yin Huilin |
| description | An algorithm based on grey innovation model GM (1, 1) of fixed length is introduced for the localization and tracking of moving targets. Kalman filter is an efficient computational method for tracking, but motion and noise assumption limits its process model to constant velocity model or constant acceleration model. The grey system theory uses the data characteristic of extrinsic randomicity and holistic regularity to find out the intrinsic rules of the system. It explores the law of subjectpsilas motivation by accumulation of raw data and builds up the differential equations to estimate the next states of the system. Therefore an object tracking algorithm based on grey innovation model GM (1, 1) of fixed length is proposed and studied in detail. The effectiveness and efficiency of the proposed method is revealed through the performance comparison of grey innovation model and Kalman filter with constant acceleration model. A further study advice is discussed at the end. |
| doi_str_mv | 10.1109/CSIE.2009.177 |
| format | Conference Proceeding |
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Kalman filter is an efficient computational method for tracking, but motion and noise assumption limits its process model to constant velocity model or constant acceleration model. The grey system theory uses the data characteristic of extrinsic randomicity and holistic regularity to find out the intrinsic rules of the system. It explores the law of subjectpsilas motivation by accumulation of raw data and builds up the differential equations to estimate the next states of the system. Therefore an object tracking algorithm based on grey innovation model GM (1, 1) of fixed length is proposed and studied in detail. The effectiveness and efficiency of the proposed method is revealed through the performance comparison of grey innovation model and Kalman filter with constant acceleration model. A further study advice is discussed at the end.</description><identifier>ISBN: 9780769535074</identifier><identifier>ISBN: 0769535070</identifier><identifier>DOI: 10.1109/CSIE.2009.177</identifier><language>eng</language><publisher>IEEE</publisher><subject>Acceleration ; Aerodynamics ; Differential equations ; grey innovation model GM ; Kalman filter ; localization and tracking ; Navigation ; Optical feedback ; Optical filters ; State estimation ; Target tracking ; Technological innovation ; Vehicle dynamics</subject><ispartof>2009 WRI World Congress on Computer Science and Information Engineering, 2009, Vol.6, p.615-618</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/5170774$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54899</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5170774$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Fu Qiang</creatorcontrib><creatorcontrib>Xiao Yunshi</creatorcontrib><creatorcontrib>Yin Huilin</creatorcontrib><title>Object Tracking Algorithm Based on Grey Innovation Model GM (1, 1) of Fixed Length</title><title>2009 WRI World Congress on Computer Science and Information Engineering</title><addtitle>CSIE</addtitle><description>An algorithm based on grey innovation model GM (1, 1) of fixed length is introduced for the localization and tracking of moving targets. Kalman filter is an efficient computational method for tracking, but motion and noise assumption limits its process model to constant velocity model or constant acceleration model. The grey system theory uses the data characteristic of extrinsic randomicity and holistic regularity to find out the intrinsic rules of the system. It explores the law of subjectpsilas motivation by accumulation of raw data and builds up the differential equations to estimate the next states of the system. Therefore an object tracking algorithm based on grey innovation model GM (1, 1) of fixed length is proposed and studied in detail. The effectiveness and efficiency of the proposed method is revealed through the performance comparison of grey innovation model and Kalman filter with constant acceleration model. A further study advice is discussed at the end.</description><subject>Acceleration</subject><subject>Aerodynamics</subject><subject>Differential equations</subject><subject>grey innovation model GM</subject><subject>Kalman filter</subject><subject>localization and tracking</subject><subject>Navigation</subject><subject>Optical feedback</subject><subject>Optical filters</subject><subject>State estimation</subject><subject>Target tracking</subject><subject>Technological innovation</subject><subject>Vehicle dynamics</subject><isbn>9780769535074</isbn><isbn>0769535070</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjE1Lw0AYhBdEUGqOnrzsUcHEd7Mf7-6xhjYGUgqae8nHbro1TSQJYv-9AZ3LzMMMQ8g9g4gxMC_JR7aJYgATMcQrEhjUgMpILgHFDQmm6QQAzCiUqG7J-7462XqmxVjWn75v6bprh9HPxzN9LSfb0KGn6WgvNOv74buc_cK7obEdTXf0kT1T9kQHR7f-Z9nmtm_n4x25dmU32eDfV6TYborkLcz3aZas89AbmEMRM1dL7mpVG4EgtI25Bu64gEZLjUxUqrENVnEdS7tICOGWgMo6ZRvNV-Th79Yv5eFr9OdyvBwkQ0AU_BfoVktx</recordid><startdate>200903</startdate><enddate>200903</enddate><creator>Fu Qiang</creator><creator>Xiao Yunshi</creator><creator>Yin Huilin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200903</creationdate><title>Object Tracking Algorithm Based on Grey Innovation Model GM (1, 1) of Fixed Length</title><author>Fu Qiang ; Xiao Yunshi ; Yin Huilin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-421fc53fc6c947048e23803f340d858714b6ded7b2c25eeee444f5ee76ef6ed83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Acceleration</topic><topic>Aerodynamics</topic><topic>Differential equations</topic><topic>grey innovation model GM</topic><topic>Kalman filter</topic><topic>localization and tracking</topic><topic>Navigation</topic><topic>Optical feedback</topic><topic>Optical filters</topic><topic>State estimation</topic><topic>Target tracking</topic><topic>Technological innovation</topic><topic>Vehicle dynamics</topic><toplevel>online_resources</toplevel><creatorcontrib>Fu Qiang</creatorcontrib><creatorcontrib>Xiao Yunshi</creatorcontrib><creatorcontrib>Yin Huilin</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>Fu Qiang</au><au>Xiao Yunshi</au><au>Yin Huilin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Object Tracking Algorithm Based on Grey Innovation Model GM (1, 1) of Fixed Length</atitle><btitle>2009 WRI World Congress on Computer Science and Information Engineering</btitle><stitle>CSIE</stitle><date>2009-03</date><risdate>2009</risdate><volume>6</volume><spage>615</spage><epage>618</epage><pages>615-618</pages><isbn>9780769535074</isbn><isbn>0769535070</isbn><abstract>An algorithm based on grey innovation model GM (1, 1) of fixed length is introduced for the localization and tracking of moving targets. Kalman filter is an efficient computational method for tracking, but motion and noise assumption limits its process model to constant velocity model or constant acceleration model. The grey system theory uses the data characteristic of extrinsic randomicity and holistic regularity to find out the intrinsic rules of the system. It explores the law of subjectpsilas motivation by accumulation of raw data and builds up the differential equations to estimate the next states of the system. Therefore an object tracking algorithm based on grey innovation model GM (1, 1) of fixed length is proposed and studied in detail. The effectiveness and efficiency of the proposed method is revealed through the performance comparison of grey innovation model and Kalman filter with constant acceleration model. A further study advice is discussed at the end.</abstract><pub>IEEE</pub><doi>10.1109/CSIE.2009.177</doi><tpages>4</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Acceleration Aerodynamics Differential equations grey innovation model GM Kalman filter localization and tracking Navigation Optical feedback Optical filters State estimation Target tracking Technological innovation Vehicle dynamics |
| title | Object Tracking Algorithm Based on Grey Innovation Model GM (1, 1) of Fixed Length |
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