Least trace extended set-membership filter
To improve the consistency of estimation result, a least-trace extended set-membership filter （LTESMF） is presented for a class of nonlinear stochastic systems, which has linear output and unknown- but-bounded noise. Feedback technique is used instead of the intersection of ellipsoid-sets in the mea...
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| Veröffentlicht in: | Science China Information Sciences 2010-02, Vol.53 (2), p.258-270 |
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| creator | Huang, Yi Chen, ZongJi Wei, Chen |
| description | To improve the consistency of estimation result, a least-trace extended set-membership filter （LTESMF） is presented for a class of nonlinear stochastic systems, which has linear output and unknown- but-bounded noise. Feedback technique is used instead of the intersection of ellipsoid-sets in the measurement update. The feedback parameter is optimized in order to minimize the trace of error bounded ellipsoid＇s envelop matrix. A new stability analysis method was developed to prove the stochastic system＇s stability by using the convergence of some measurement of the error bounded ellipsoid. Analysis result shows that the estimation error of LTESMF will converge to a bounded area. A simulation of SINS/GPS integrated alignment with large misalignment angles is conducted. The results demonstrate that the convergence speed and the consistency of LTESMF are much better than those of extended Kalman filter （EKF）, in addition the steady estimation precision and computational complexity are close to that of EKF. |
| doi_str_mv | 10.1007/s11432-010-0024-x |
| format | Article |
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Feedback technique is used instead of the intersection of ellipsoid-sets in the measurement update. The feedback parameter is optimized in order to minimize the trace of error bounded ellipsoid＇s envelop matrix. A new stability analysis method was developed to prove the stochastic system＇s stability by using the convergence of some measurement of the error bounded ellipsoid. Analysis result shows that the estimation error of LTESMF will converge to a bounded area. A simulation of SINS/GPS integrated alignment with large misalignment angles is conducted. The results demonstrate that the convergence speed and the consistency of LTESMF are much better than those of extended Kalman filter （EKF）, in addition the steady estimation precision and computational complexity are close to that of EKF.</description><identifier>ISSN: 1674-733X</identifier><identifier>EISSN: 1869-1919</identifier><identifier>EISSN: 1862-2836</identifier><identifier>DOI: 10.1007/s11432-010-0024-x</identifier><language>eng</language><publisher>Heidelberg: SP Science in China Press</publisher><subject>Computer Science ; Computer simulation ; Consistency ; Convergence ; Error analysis ; Extended Kalman filter ; Feedback ; Geographic information systems ; Global Positioning System ; Information Systems and Communication Service ; Misalignment ; Noise (mathematics) ; Nonlinear systems ; Research Papers ; Satellite navigation systems ; Stability analysis ; Stochastic systems ; 扩展卡尔曼滤波 ; 收敛速度 ; 测量系统 ; 稳定分析方法 ; 错误跟踪 ; 非线性随机系统</subject><ispartof>Science China Information Sciences, 2010-02, Vol.53 (2), p.258-270</ispartof><rights>Science in China Press and Springer Berlin Heidelberg 2010</rights><rights>Science in China Press and Springer Berlin Heidelberg 2010.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-ff59a2654808ff69514ac476d832184ae310d590c6be8e88ba972994b5e707f13</citedby><cites>FETCH-LOGICAL-c376t-ff59a2654808ff69514ac476d832184ae310d590c6be8e88ba972994b5e707f13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/84009A/84009A.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11432-010-0024-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918625816?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>315,781,785,21393,27929,27930,33749,33750,41493,42562,43810,51324,64390,64392,64394,72474</link.rule.ids></links><search><creatorcontrib>Huang, Yi</creatorcontrib><creatorcontrib>Chen, ZongJi</creatorcontrib><creatorcontrib>Wei, Chen</creatorcontrib><title>Least trace extended set-membership filter</title><title>Science China Information Sciences</title><addtitle>Sci. China Inf. Sci</addtitle><addtitle>SCIENCE CHINA Information Sciences</addtitle><description>To improve the consistency of estimation result, a least-trace extended set-membership filter （LTESMF） is presented for a class of nonlinear stochastic systems, which has linear output and unknown- but-bounded noise. Feedback technique is used instead of the intersection of ellipsoid-sets in the measurement update. The feedback parameter is optimized in order to minimize the trace of error bounded ellipsoid＇s envelop matrix. A new stability analysis method was developed to prove the stochastic system＇s stability by using the convergence of some measurement of the error bounded ellipsoid. Analysis result shows that the estimation error of LTESMF will converge to a bounded area. A simulation of SINS/GPS integrated alignment with large misalignment angles is conducted. The results demonstrate that the convergence speed and the consistency of LTESMF are much better than those of extended Kalman filter （EKF）, in addition the steady estimation precision and computational complexity are close to that of EKF.</description><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Consistency</subject><subject>Convergence</subject><subject>Error analysis</subject><subject>Extended Kalman filter</subject><subject>Feedback</subject><subject>Geographic information systems</subject><subject>Global Positioning System</subject><subject>Information Systems and Communication Service</subject><subject>Misalignment</subject><subject>Noise (mathematics)</subject><subject>Nonlinear systems</subject><subject>Research Papers</subject><subject>Satellite navigation systems</subject><subject>Stability analysis</subject><subject>Stochastic systems</subject><subject>扩展卡尔曼滤波</subject><subject>收敛速度</subject><subject>测量系统</subject><subject>稳定分析方法</subject><subject>错误跟踪</subject><subject>非线性随机系统</subject><issn>1674-733X</issn><issn>1869-1919</issn><issn>1862-2836</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1LAzEQhhdRsNT-AG-LXkSIZvKxSY5S_IKCFwVvIbs7227djzbZQv33pmxB8GAYSA7PO5N5kuQS6B1Qqu4DgOCMUKCEUibI_iSZgM4MAQPmNL4zJYji_PM8mYWwpvFwTpnSk-R2gS4M6eBdgSnuB-xKLNOAA2mxzdGHVb1Jq7oZ0F8kZ5VrAs6O9zT5eHp8n7-Qxdvz6_xhQQqusoFUlTSOZVJoqqsqMxKEK4TKSs0ZaOGQAy2loUWWo0atc2cUM0bkEhVVFfBpcjP23fh-u8Mw2LYOBTaN67DfBRu3AS5iyYhe_0HX_c538XeWmWiASQ1ZpGCkCt-H4LGyG1-3zn9boPYg0I4CbRRoDwLtPmbYmAmR7Zbofzv_F7o6Dlr13XIbczZ3xVfUhzYKlwI05T9ZH3vq</recordid><startdate>20100201</startdate><enddate>20100201</enddate><creator>Huang, Yi</creator><creator>Chen, ZongJi</creator><creator>Wei, Chen</creator><general>SP Science in China Press</general><general>Springer Nature B.V</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7SC</scope><scope>8FD</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20100201</creationdate><title>Least trace extended set-membership filter</title><author>Huang, Yi ; Chen, ZongJi ; Wei, Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-ff59a2654808ff69514ac476d832184ae310d590c6be8e88ba972994b5e707f13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Computer Science</topic><topic>Computer simulation</topic><topic>Consistency</topic><topic>Convergence</topic><topic>Error analysis</topic><topic>Extended Kalman filter</topic><topic>Feedback</topic><topic>Geographic information systems</topic><topic>Global Positioning System</topic><topic>Information Systems and Communication Service</topic><topic>Misalignment</topic><topic>Noise (mathematics)</topic><topic>Nonlinear systems</topic><topic>Research Papers</topic><topic>Satellite navigation systems</topic><topic>Stability analysis</topic><topic>Stochastic systems</topic><topic>扩展卡尔曼滤波</topic><topic>收敛速度</topic><topic>测量系统</topic><topic>稳定分析方法</topic><topic>错误跟踪</topic><topic>非线性随机系统</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Yi</creatorcontrib><creatorcontrib>Chen, ZongJi</creatorcontrib><creatorcontrib>Wei, Chen</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Science China Information Sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Yi</au><au>Chen, ZongJi</au><au>Wei, Chen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Least trace extended set-membership filter</atitle><jtitle>Science China Information Sciences</jtitle><stitle>Sci. China Inf. Sci</stitle><addtitle>SCIENCE CHINA Information Sciences</addtitle><date>2010-02-01</date><risdate>2010</risdate><volume>53</volume><issue>2</issue><spage>258</spage><epage>270</epage><pages>258-270</pages><issn>1674-733X</issn><eissn>1869-1919</eissn><eissn>1862-2836</eissn><abstract>To improve the consistency of estimation result, a least-trace extended set-membership filter （LTESMF） is presented for a class of nonlinear stochastic systems, which has linear output and unknown- but-bounded noise. Feedback technique is used instead of the intersection of ellipsoid-sets in the measurement update. The feedback parameter is optimized in order to minimize the trace of error bounded ellipsoid＇s envelop matrix. A new stability analysis method was developed to prove the stochastic system＇s stability by using the convergence of some measurement of the error bounded ellipsoid. Analysis result shows that the estimation error of LTESMF will converge to a bounded area. A simulation of SINS/GPS integrated alignment with large misalignment angles is conducted. The results demonstrate that the convergence speed and the consistency of LTESMF are much better than those of extended Kalman filter （EKF）, in addition the steady estimation precision and computational complexity are close to that of EKF.</abstract><cop>Heidelberg</cop><pub>SP Science in China Press</pub><doi>10.1007/s11432-010-0024-x</doi><tpages>13</tpages></addata></record> |
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| subjects | Computer Science Computer simulation Consistency Convergence Error analysis Extended Kalman filter Feedback Geographic information systems Global Positioning System Information Systems and Communication Service Misalignment Noise (mathematics) Nonlinear systems Research Papers Satellite navigation systems Stability analysis Stochastic systems 扩展卡尔曼滤波 收敛速度 测量系统 稳定分析方法 错误跟踪 非线性随机系统 |
| title | Least trace extended set-membership filter |
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