Guaranteed Robust Nonlinear State Estimator with Application to Global Vehicle Tracking
This paper deals with guaranteed recursive state estimation in a bounded-error context with application to global dynamical vehicle tracking. As in Kalman or approximate Bayesian filtering, prediction and correction phases alternate. A distinctive feature of the method advocated here is that its res...
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| creator | Kieffer, M. Seignez, E. Lambert, A. Walter, E. Maurin, T. |
| description | This paper deals with guaranteed recursive state estimation in a bounded-error context with application to global dynamical vehicle tracking. As in Kalman or approximate Bayesian filtering, prediction and correction phases alternate. A distinctive feature of the method advocated here is that its results are guaranteed, in the sense that the statements made about the possible values of the state vector are mathmatically proved, although all calculations are performed approximately on a computer. Sets will thus be provided that are guaranteed to contain all values of the state that are consistent with the information available and the bounds assumed on the state perturbations and measurement errors. Complexity issues are addressed and some tools are provided to facilitate real-time implementation. Results obtained with an actual vehicle are reported. |
| doi_str_mv | 10.1109/CDC.2005.1583192 |
| format | Conference Proceeding |
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Results obtained with an actual vehicle are reported.</description><subject>Bayesian methods</subject><subject>Density measurement</subject><subject>Equations</subject><subject>Filtering</subject><subject>Kalman filters</subject><subject>Noise measurement</subject><subject>Robustness</subject><subject>State estimation</subject><subject>Time measurement</subject><subject>Vehicles</subject><issn>0191-2216</issn><isbn>9780780395671</isbn><isbn>0780395670</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkMtKAzEYRgMqWGv3gpu8wNRcJrdlGesoFAWtuizJ5K-NxsmQSRHf3oKFD87ucPgQuqJkTikxN81tM2eEiDkVmlPDTtDMKE0O40ZIRU_RhFBDK8aoPEcX4_hJCNFEygl6b_c2274AePyc3H4s-DH1MfRgM34ptgBejiV825Iy_gllhxfDEENnS0g9Lgm3MTkb8RvsQhcBr7PtvkL_cYnOtjaOMDtyil7vluvmvlo9tQ_NYlUFqkSpJJNO8bp2NZM1GOW2TnNlnHGMCuGF54x4QazrjHdUMK-1EsowA9BpbS2fout_bwCAzZAPpfl3c_yB_wGINVFQ</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Kieffer, M.</creator><creator>Seignez, E.</creator><creator>Lambert, A.</creator><creator>Walter, E.</creator><creator>Maurin, T.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>Guaranteed Robust Nonlinear State Estimator with Application to Global Vehicle Tracking</title><author>Kieffer, M. ; Seignez, E. ; Lambert, A. ; Walter, E. ; Maurin, T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-626b7344b4264e97bfb8379b9b2155d5d320d50abc9db152d88757929eec88aa3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Bayesian methods</topic><topic>Density measurement</topic><topic>Equations</topic><topic>Filtering</topic><topic>Kalman filters</topic><topic>Noise measurement</topic><topic>Robustness</topic><topic>State estimation</topic><topic>Time measurement</topic><topic>Vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Kieffer, M.</creatorcontrib><creatorcontrib>Seignez, E.</creatorcontrib><creatorcontrib>Lambert, A.</creatorcontrib><creatorcontrib>Walter, E.</creatorcontrib><creatorcontrib>Maurin, T.</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>Kieffer, M.</au><au>Seignez, E.</au><au>Lambert, A.</au><au>Walter, E.</au><au>Maurin, T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Guaranteed Robust Nonlinear State Estimator with Application to Global Vehicle Tracking</atitle><btitle>Proceedings of the 44th IEEE Conference on Decision and Control</btitle><stitle>CDC</stitle><date>2005</date><risdate>2005</risdate><spage>6424</spage><epage>6429</epage><pages>6424-6429</pages><issn>0191-2216</issn><isbn>9780780395671</isbn><isbn>0780395670</isbn><abstract>This paper deals with guaranteed recursive state estimation in a bounded-error context with application to global dynamical vehicle tracking. As in Kalman or approximate Bayesian filtering, prediction and correction phases alternate. A distinctive feature of the method advocated here is that its results are guaranteed, in the sense that the statements made about the possible values of the state vector are mathmatically proved, although all calculations are performed approximately on a computer. Sets will thus be provided that are guaranteed to contain all values of the state that are consistent with the information available and the bounds assumed on the state perturbations and measurement errors. Complexity issues are addressed and some tools are provided to facilitate real-time implementation. Results obtained with an actual vehicle are reported.</abstract><pub>IEEE</pub><doi>10.1109/CDC.2005.1583192</doi><tpages>6</tpages></addata></record> |
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| language | eng |
| recordid | cdi_ieee_primary_1583192 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Density measurement Equations Filtering Kalman filters Noise measurement Robustness State estimation Time measurement Vehicles |
| title | Guaranteed Robust Nonlinear State Estimator with Application to Global Vehicle Tracking |
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