Random delayed Kalman fusion based on equivalent conversion for the multisensor system
Aiming at the linear time invariant or the parameters preliminary given for the multisensor target tracking system, this paper develops a random delayed Kalman filter fusion estimator based on equivalent conversion for multisensor system. This algorithm effectively uses the characteristics of Kalman...
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| creator | Chenlin Wen Tingliang Xu Quanbo Ge |
| description | Aiming at the linear time invariant or the parameters preliminary given for the multisensor target tracking system, this paper develops a random delayed Kalman filter fusion estimator based on equivalent conversion for multisensor system. This algorithm effectively uses the characteristics of Kalman filter statistical parameters which can be calculated out of line, and the calculated form of measurements weighted summation under Linear Minimum Mean Square Error (LMMSE) estimate. Firstly, it transformed the multisensor system into the single sensor order form based on the idea of remolding. Secondly, it utilizes the method of one step prediction estimate and measurements prediction residual compensation to get the optimal weighting coefficient though calculating out of line and adjusting online. Then, it can realize the optimal update of the random delay measurements. At last, Algorithm analysis and computer simulation indicates that this algorithm is validity and advantage. |
| doi_str_mv | 10.1109/CCDC.2011.5968877 |
| format | Conference Proceeding |
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This algorithm effectively uses the characteristics of Kalman filter statistical parameters which can be calculated out of line, and the calculated form of measurements weighted summation under Linear Minimum Mean Square Error (LMMSE) estimate. Firstly, it transformed the multisensor system into the single sensor order form based on the idea of remolding. Secondly, it utilizes the method of one step prediction estimate and measurements prediction residual compensation to get the optimal weighting coefficient though calculating out of line and adjusting online. Then, it can realize the optimal update of the random delay measurements. At last, Algorithm analysis and computer simulation indicates that this algorithm is validity and advantage.</description><identifier>ISSN: 1948-9439</identifier><identifier>ISBN: 9781424487370</identifier><identifier>ISBN: 1424487374</identifier><identifier>EISSN: 1948-9447</identifier><identifier>EISBN: 9781424487363</identifier><identifier>EISBN: 1424487382</identifier><identifier>EISBN: 9781424487387</identifier><identifier>EISBN: 1424487366</identifier><identifier>DOI: 10.1109/CCDC.2011.5968877</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Delay ; Kalman filter ; Kalman filters ; measurements summation ; multisensor system ; Multisensor systems ; Prediction algorithms ; prediction and compensation ; random delay ; Weight measurement</subject><ispartof>2011 Chinese Control and Decision Conference (CCDC), 2011, p.3755-3760</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/5968877$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5968877$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chenlin Wen</creatorcontrib><creatorcontrib>Tingliang Xu</creatorcontrib><creatorcontrib>Quanbo Ge</creatorcontrib><title>Random delayed Kalman fusion based on equivalent conversion for the multisensor system</title><title>2011 Chinese Control and Decision Conference (CCDC)</title><addtitle>CCDC</addtitle><description>Aiming at the linear time invariant or the parameters preliminary given for the multisensor target tracking system, this paper develops a random delayed Kalman filter fusion estimator based on equivalent conversion for multisensor system. This algorithm effectively uses the characteristics of Kalman filter statistical parameters which can be calculated out of line, and the calculated form of measurements weighted summation under Linear Minimum Mean Square Error (LMMSE) estimate. Firstly, it transformed the multisensor system into the single sensor order form based on the idea of remolding. Secondly, it utilizes the method of one step prediction estimate and measurements prediction residual compensation to get the optimal weighting coefficient though calculating out of line and adjusting online. Then, it can realize the optimal update of the random delay measurements. At last, Algorithm analysis and computer simulation indicates that this algorithm is validity and advantage.</description><subject>Algorithm design and analysis</subject><subject>Delay</subject><subject>Kalman filter</subject><subject>Kalman filters</subject><subject>measurements summation</subject><subject>multisensor system</subject><subject>Multisensor systems</subject><subject>Prediction algorithms</subject><subject>prediction and compensation</subject><subject>random delay</subject><subject>Weight measurement</subject><issn>1948-9439</issn><issn>1948-9447</issn><isbn>9781424487370</isbn><isbn>1424487374</isbn><isbn>9781424487363</isbn><isbn>1424487382</isbn><isbn>9781424487387</isbn><isbn>1424487366</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkEtLxEAQhMcXuKz7A8TL_IGsPe-Zo8QnLgiiXpdO0oORPDSTXdh_b9BFsC7VVR_0oRg7F7AUAsJlnl_nSwlCLE2w3jt3wBbBeaGl1t4pqw7ZTATts6C1O_rHHBz_MRVO2SKlD5hkbQAZZuztGbuqb3lFDe6o4o_YtNjxuEl13_EC09RNB31t6i021I287LstDT849gMf34m3m2asE3VpymmXRmrP2EnEJtFi73P2envzkt9nq6e7h_xqldXCmTErKxmlsQQQXam8J6zKWIkCCq_IehdMRImFDhZMIQNSqQAMAGFwxgCpObv4_VsT0fpzqFscduv9SuoboadYEA</recordid><startdate>201105</startdate><enddate>201105</enddate><creator>Chenlin Wen</creator><creator>Tingliang Xu</creator><creator>Quanbo Ge</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201105</creationdate><title>Random delayed Kalman fusion based on equivalent conversion for the multisensor system</title><author>Chenlin Wen ; Tingliang Xu ; Quanbo Ge</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-cd2f256e00f7c388eadcfd1b0b83e68795fa2ab49605b29aec300500ea97550e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithm design and analysis</topic><topic>Delay</topic><topic>Kalman filter</topic><topic>Kalman filters</topic><topic>measurements summation</topic><topic>multisensor system</topic><topic>Multisensor systems</topic><topic>Prediction algorithms</topic><topic>prediction and compensation</topic><topic>random delay</topic><topic>Weight measurement</topic><toplevel>online_resources</toplevel><creatorcontrib>Chenlin Wen</creatorcontrib><creatorcontrib>Tingliang Xu</creatorcontrib><creatorcontrib>Quanbo Ge</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>Chenlin Wen</au><au>Tingliang Xu</au><au>Quanbo Ge</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Random delayed Kalman fusion based on equivalent conversion for the multisensor system</atitle><btitle>2011 Chinese Control and Decision Conference (CCDC)</btitle><stitle>CCDC</stitle><date>2011-05</date><risdate>2011</risdate><spage>3755</spage><epage>3760</epage><pages>3755-3760</pages><issn>1948-9439</issn><eissn>1948-9447</eissn><isbn>9781424487370</isbn><isbn>1424487374</isbn><eisbn>9781424487363</eisbn><eisbn>1424487382</eisbn><eisbn>9781424487387</eisbn><eisbn>1424487366</eisbn><abstract>Aiming at the linear time invariant or the parameters preliminary given for the multisensor target tracking system, this paper develops a random delayed Kalman filter fusion estimator based on equivalent conversion for multisensor system. This algorithm effectively uses the characteristics of Kalman filter statistical parameters which can be calculated out of line, and the calculated form of measurements weighted summation under Linear Minimum Mean Square Error (LMMSE) estimate. Firstly, it transformed the multisensor system into the single sensor order form based on the idea of remolding. Secondly, it utilizes the method of one step prediction estimate and measurements prediction residual compensation to get the optimal weighting coefficient though calculating out of line and adjusting online. Then, it can realize the optimal update of the random delay measurements. At last, Algorithm analysis and computer simulation indicates that this algorithm is validity and advantage.</abstract><pub>IEEE</pub><doi>10.1109/CCDC.2011.5968877</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2011 Chinese Control and Decision Conference (CCDC), 2011, p.3755-3760 |
| issn | 1948-9439 1948-9447 |
| language | eng |
| recordid | cdi_ieee_primary_5968877 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Delay Kalman filter Kalman filters measurements summation multisensor system Multisensor systems Prediction algorithms prediction and compensation random delay Weight measurement |
| title | Random delayed Kalman fusion based on equivalent conversion for the multisensor system |
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