Fusion estimation of dynamic system based on multi-speed sensor
A kind of multi-sensor system reconstruct algorithm is given based on Kalman filter, which combines multi-resolution analysis method, traditional Kalman filter and multi-sensor data fusion, and this algorithm points to the multi-speed sensor which detect the same target's state and have the mul...
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| creator | Feng Lv Xiu-Qing Wang Hai-Lian Du Yuan Li |
| description | A kind of multi-sensor system reconstruct algorithm is given based on Kalman filter, which combines multi-resolution analysis method, traditional Kalman filter and multi-sensor data fusion, and this algorithm points to the multi-speed sensor which detect the same target's state and have the multi-scale single mode dynamic system. The algorithm improves the system's estimative accuracy, and have strong robust on the abrupt unknown interference, which use the local wavelet pre-processing technology, get the optimal estimative value on the thinnest scale based on the global observing information. The algorithm's effectiveness is proved through computer simulation. |
| doi_str_mv | 10.1109/ICMLC.2005.1527116 |
| format | Conference Proceeding |
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The algorithm improves the system's estimative accuracy, and have strong robust on the abrupt unknown interference, which use the local wavelet pre-processing technology, get the optimal estimative value on the thinnest scale based on the global observing information. The algorithm's effectiveness is proved through computer simulation.</description><identifier>ISSN: 2160-133X</identifier><identifier>ISBN: 0780390911</identifier><identifier>ISBN: 9780780390911</identifier><identifier>DOI: 10.1109/ICMLC.2005.1527116</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Chemical sensors ; Computer simulation ; dynamic system ; fusion estimation ; Geophysical measurements ; Interference ; Kalman filter ; Multi-scale ; Robustness ; Sea measurements ; Sensor fusion ; Sensor phenomena and characterization ; Sensor systems</subject><ispartof>2005 International Conference on Machine Learning and Cybernetics, 2005, Vol.2, p.1148-1153 Vol. 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/1527116$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,4048,4049,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1527116$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Feng Lv</creatorcontrib><creatorcontrib>Xiu-Qing Wang</creatorcontrib><creatorcontrib>Hai-Lian Du</creatorcontrib><creatorcontrib>Yuan Li</creatorcontrib><title>Fusion estimation of dynamic system based on multi-speed sensor</title><title>2005 International Conference on Machine Learning and Cybernetics</title><addtitle>ICMLC</addtitle><description>A kind of multi-sensor system reconstruct algorithm is given based on Kalman filter, which combines multi-resolution analysis method, traditional Kalman filter and multi-sensor data fusion, and this algorithm points to the multi-speed sensor which detect the same target's state and have the multi-scale single mode dynamic system. The algorithm improves the system's estimative accuracy, and have strong robust on the abrupt unknown interference, which use the local wavelet pre-processing technology, get the optimal estimative value on the thinnest scale based on the global observing information. The algorithm's effectiveness is proved through computer simulation.</description><subject>Algorithm design and analysis</subject><subject>Chemical sensors</subject><subject>Computer simulation</subject><subject>dynamic system</subject><subject>fusion estimation</subject><subject>Geophysical measurements</subject><subject>Interference</subject><subject>Kalman filter</subject><subject>Multi-scale</subject><subject>Robustness</subject><subject>Sea measurements</subject><subject>Sensor fusion</subject><subject>Sensor phenomena and characterization</subject><subject>Sensor systems</subject><issn>2160-133X</issn><isbn>0780390911</isbn><isbn>9780780390911</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT8FKw0AUXFDBWvsDeskPJL63L5vdPYkEWwsRLwreyib7AitNUrLpIX9vxM5lZhgYZoR4QMgQwT7ty_eqzCSAylBJjVhciTvQBsiCRbwWK4kFpEj0fSs2Mf7AArKqIFiJ5-05hqFPOE6hc9OfHNrEz73rQpPEOU7cJbWL7JMl6s7HKaTxxIuN3MdhvBc3rTtG3lx4Lb62r5_lW1p97PblS5UGBDWljWHlcqVAWp1rV7QaGiKfs0cpc21a1SrjC6-ZCBtNjNLkZGvPYJhUTWvx-N8bmPlwGpex43y4_KVfzjhJEg</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Feng Lv</creator><creator>Xiu-Qing Wang</creator><creator>Hai-Lian Du</creator><creator>Yuan Li</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2005</creationdate><title>Fusion estimation of dynamic system based on multi-speed sensor</title><author>Feng Lv ; Xiu-Qing Wang ; Hai-Lian Du ; Yuan Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i105t-c8e5a455029747a6f70c33d4ed122478f5f58d6d7e331c73e128439bde08e35b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithm design and analysis</topic><topic>Chemical sensors</topic><topic>Computer simulation</topic><topic>dynamic system</topic><topic>fusion estimation</topic><topic>Geophysical measurements</topic><topic>Interference</topic><topic>Kalman filter</topic><topic>Multi-scale</topic><topic>Robustness</topic><topic>Sea measurements</topic><topic>Sensor fusion</topic><topic>Sensor phenomena and characterization</topic><topic>Sensor systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Feng Lv</creatorcontrib><creatorcontrib>Xiu-Qing Wang</creatorcontrib><creatorcontrib>Hai-Lian Du</creatorcontrib><creatorcontrib>Yuan Li</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>Feng Lv</au><au>Xiu-Qing Wang</au><au>Hai-Lian Du</au><au>Yuan Li</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fusion estimation of dynamic system based on multi-speed sensor</atitle><btitle>2005 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2005</date><risdate>2005</risdate><volume>2</volume><spage>1148</spage><epage>1153 Vol. 2</epage><pages>1148-1153 Vol. 2</pages><issn>2160-133X</issn><isbn>0780390911</isbn><isbn>9780780390911</isbn><abstract>A kind of multi-sensor system reconstruct algorithm is given based on Kalman filter, which combines multi-resolution analysis method, traditional Kalman filter and multi-sensor data fusion, and this algorithm points to the multi-speed sensor which detect the same target's state and have the multi-scale single mode dynamic system. The algorithm improves the system's estimative accuracy, and have strong robust on the abrupt unknown interference, which use the local wavelet pre-processing technology, get the optimal estimative value on the thinnest scale based on the global observing information. The algorithm's effectiveness is proved through computer simulation.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2005.1527116</doi></addata></record> |
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| ispartof | 2005 International Conference on Machine Learning and Cybernetics, 2005, Vol.2, p.1148-1153 Vol. 2 |
| issn | 2160-133X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Chemical sensors Computer simulation dynamic system fusion estimation Geophysical measurements Interference Kalman filter Multi-scale Robustness Sea measurements Sensor fusion Sensor phenomena and characterization Sensor systems |
| title | Fusion estimation of dynamic system based on multi-speed sensor |
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