An efficient approach to highly non-linear estimation
This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques the proposed recursive algorithm...
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| creator | Ruiz, V.F. |
| description | This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes' theorem. Through simulation on a generic exponential model, the proposed nonlinear filter proves to be superior to the extended Kalman filter and a class of nonlinear filters based on the partitioning algorithm. |
| doi_str_mv | 10.1109/ICDSP.2002.1028196 |
| format | Conference Proceeding |
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The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes' theorem. Through simulation on a generic exponential model, the proposed nonlinear filter proves to be superior to the extended Kalman filter and a class of nonlinear filters based on the partitioning algorithm.</description><identifier>ISBN: 9780780375031</identifier><identifier>ISBN: 0780375033</identifier><identifier>DOI: 10.1109/ICDSP.2002.1028196</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptive filters ; Density functional theory ; Distributed computing ; Entropy ; Filtering ; Lagrangian functions ; Least squares approximation ; Nonlinear equations ; Partitioning algorithms ; Probability distribution</subject><ispartof>2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. 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The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes' theorem. Through simulation on a generic exponential model, the proposed nonlinear filter proves to be superior to the extended Kalman filter and a class of nonlinear filters based on the partitioning algorithm.</description><subject>Adaptive filters</subject><subject>Density functional theory</subject><subject>Distributed computing</subject><subject>Entropy</subject><subject>Filtering</subject><subject>Lagrangian functions</subject><subject>Least squares approximation</subject><subject>Nonlinear equations</subject><subject>Partitioning algorithms</subject><subject>Probability distribution</subject><isbn>9780780375031</isbn><isbn>0780375033</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tqwzAURAWl0JL6B9qNfsCurt5eBvcVCKTQZB0U5d5axZWN7U3-voZmGJjNYYZh7BFEBSDq503z8vVZSSFkBUJ6qO0NK2rnxWLljFBwx4pp-hGLtNHeuXtm1pkjUYoJ88zDMIx9iC2fe96m77a78NznsksZw8hxmtNvmFOfH9gthW7C4pordnh73Tcf5Xb3vmnW2zKBM3Npgw-WECWeycPJoj35oI2QmpS35EKNKkbUoC1EQZHgrEjTApCyRkm1Yk__vQkRj8O4zI-X4_Wc-gNgskUD</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Ruiz, V.F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2002</creationdate><title>An efficient approach to highly non-linear estimation</title><author>Ruiz, V.F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-6a8a6fee2edf81b6e6b8a45024f386f7a9e3cce41461c0fcf1d3f4f502f365323</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Adaptive filters</topic><topic>Density functional theory</topic><topic>Distributed computing</topic><topic>Entropy</topic><topic>Filtering</topic><topic>Lagrangian functions</topic><topic>Least squares approximation</topic><topic>Nonlinear equations</topic><topic>Partitioning algorithms</topic><topic>Probability distribution</topic><toplevel>online_resources</toplevel><creatorcontrib>Ruiz, V.F.</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>Ruiz, V.F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An efficient approach to highly non-linear estimation</atitle><btitle>2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628)</btitle><stitle>ICDSP</stitle><date>2002</date><risdate>2002</risdate><volume>2</volume><spage>737</spage><epage>740 vol.2</epage><pages>737-740 vol.2</pages><isbn>9780780375031</isbn><isbn>0780375033</isbn><abstract>This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes' theorem. Through simulation on a generic exponential model, the proposed nonlinear filter proves to be superior to the extended Kalman filter and a class of nonlinear filters based on the partitioning algorithm.</abstract><pub>IEEE</pub><doi>10.1109/ICDSP.2002.1028196</doi></addata></record> |
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| ispartof | 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628), 2002, Vol.2, p.737-740 vol.2 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive filters Density functional theory Distributed computing Entropy Filtering Lagrangian functions Least squares approximation Nonlinear equations Partitioning algorithms Probability distribution |
| title | An efficient approach to highly non-linear estimation |
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