An innovations approach to least squares estimation--Part IV: Recursive estimation given lumped covariance functions
We show how to recursively compute linear least squares filtered and smoothed estimates for a lumped signal process in additive white noise. However, unlike the Kalman-Bucy problem, here only the covariance function of the signal process is known and not a specific state-variable model. The solution...
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Veröffentlicht in: | IEEE transactions on automatic control 1971-12, Vol.16 (6), p.720-727 |
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container_title | IEEE transactions on automatic control |
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creator | Kailath, T. Geesey, R. |
description | We show how to recursively compute linear least squares filtered and smoothed estimates for a lumped signal process in additive white noise. However, unlike the Kalman-Bucy problem, here only the covariance function of the signal process is known and not a specific state-variable model. The solutions are based on the innovations representation for the observation process. |
doi_str_mv | 10.1109/TAC.1971.1099835 |
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The solutions are based on the innovations representation for the observation process.</description><subject>Additive white noise</subject><subject>Contracts</subject><subject>Laboratories</subject><subject>Least squares approximation</subject><subject>Least squares methods</subject><subject>Nonlinear filters</subject><subject>Recursive estimation</subject><subject>Signal processing</subject><subject>Technological innovation</subject><subject>Transfer functions</subject><issn>0018-9286</issn><issn>1558-2523</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1971</creationdate><recordtype>article</recordtype><recordid>eNqNkTtPwzAUhS0EEqWwI7F4YkvxI05stqriUakSCBVWy3FuICh1UjupxL_HbTowMl0d3e8-jg5C15TMKCXqbj1fzKjK6SwKJbk4QRMqhEyYYPwUTQihMlFMZufoIoTvKLM0pRPUzx2unWt3pq9bF7DpOt8a-4X7FjdgQo_DdjAeAobQ15sDlSSvxvd4-XGP38AOPtQ7-NPGn1E73AybDkps42pfG2cBV4OzhyuX6KwyTYCrY52i98eH9eI5Wb08LRfzVWI5531CK1JYmxcVsaZQXFVpUYhSEGFzLk1u8qxgjBkuFSGcCUJYxsqipBQywzMr-RTdjnujp-0QP9SbOlhoGuOgHYJmUslcCfEPMOUZzWkEyQha34bgodKdj7b9j6ZE73PQMQe9z0Efc4gjN-NIDQB_8LH7CyQbheE</recordid><startdate>19711201</startdate><enddate>19711201</enddate><creator>Kailath, T.</creator><creator>Geesey, R.</creator><general>IEEE</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>H8D</scope></search><sort><creationdate>19711201</creationdate><title>An innovations approach to least squares estimation--Part IV: Recursive estimation given lumped covariance functions</title><author>Kailath, T. ; Geesey, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-1f0bcc7bf0cab939f4bb5d505c738a7a76b222a3890032500262dbd11e6a36c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1971</creationdate><topic>Additive white noise</topic><topic>Contracts</topic><topic>Laboratories</topic><topic>Least squares approximation</topic><topic>Least squares methods</topic><topic>Nonlinear filters</topic><topic>Recursive estimation</topic><topic>Signal processing</topic><topic>Technological innovation</topic><topic>Transfer functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kailath, T.</creatorcontrib><creatorcontrib>Geesey, R.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Aerospace Database</collection><jtitle>IEEE transactions on automatic control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kailath, T.</au><au>Geesey, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An innovations approach to least squares estimation--Part IV: Recursive estimation given lumped covariance functions</atitle><jtitle>IEEE transactions on automatic control</jtitle><stitle>TAC</stitle><date>1971-12-01</date><risdate>1971</risdate><volume>16</volume><issue>6</issue><spage>720</spage><epage>727</epage><pages>720-727</pages><issn>0018-9286</issn><eissn>1558-2523</eissn><coden>IETAA9</coden><abstract>We show how to recursively compute linear least squares filtered and smoothed estimates for a lumped signal process in additive white noise. However, unlike the Kalman-Bucy problem, here only the covariance function of the signal process is known and not a specific state-variable model. The solutions are based on the innovations representation for the observation process.</abstract><pub>IEEE</pub><doi>10.1109/TAC.1971.1099835</doi><tpages>8</tpages></addata></record> |
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subjects | Additive white noise Contracts Laboratories Least squares approximation Least squares methods Nonlinear filters Recursive estimation Signal processing Technological innovation Transfer functions |
title | An innovations approach to least squares estimation--Part IV: Recursive estimation given lumped covariance functions |
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