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Order-recursive FIR smoothers
This paper introduces order-recursive FIR smoothers and shows that order-recursive FIR filters are special forms that occur when no future data values are used to estimate the signal. The formulation leads naturally to generalizations of the concepts of prediction-error basis and Cholesky factorizat...
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| Published in: | IEEE transactions on signal processing 1994-05, Vol.42 (5), p.1242-1246 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| Online Access: | Get full text |
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| cited_by | cdi_FETCH-LOGICAL-c306t-c6b554702955eba32c3aa9e65062431f83781edef130c376cde76f5039b2160f3 |
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| cites | cdi_FETCH-LOGICAL-c306t-c6b554702955eba32c3aa9e65062431f83781edef130c376cde76f5039b2160f3 |
| container_end_page | 1246 |
| container_issue | 5 |
| container_start_page | 1242 |
| container_title | IEEE transactions on signal processing |
| container_volume | 42 |
| creator | Jenq-Tay Yuan Stuller, J.A. |
| description | This paper introduces order-recursive FIR smoothers and shows that order-recursive FIR filters are special forms that occur when no future data values are used to estimate the signal. The formulation leads naturally to generalizations of the concepts of prediction-error basis and Cholesky factorization which are well known in FIR filter design.< > |
| doi_str_mv | 10.1109/78.295191 |
| format | article |
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The formulation leads naturally to generalizations of the concepts of prediction-error basis and Cholesky factorization which are well known in FIR filter design.< ></description><subject>Adaptive filters</subject><subject>Applied sciences</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Estimation error</subject><subject>Exact sciences and technology</subject><subject>Filtering</subject><subject>Finite impulse response filter</subject><subject>Information, signal and communications theory</subject><subject>Kalman filters</subject><subject>Lattices</subject><subject>Maximum likelihood detection</subject><subject>Nonlinear filters</subject><subject>Parameter estimation</subject><subject>Signal and communications theory</subject><subject>Signal processing algorithms</subject><subject>Signal, noise</subject><subject>Telecommunications and information theory</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1994</creationdate><recordtype>article</recordtype><recordid>eNo9kE1LAzEQhoMoWKsHr4LQgwgets5svo9SrBYKBVHwFtJ0givbbk22gv_eLVt6moF53mfgZewaYYwI9lGbcWklWjxhA7QCCxBanXY7SF5Ioz_P2UXO3wAohFUDdrtIK0pForBLufql0XT2Nsrrpmm_KOVLdhZ9nenqMIfsY_r8Pnkt5ouX2eRpXgQOqi2CWkopNHSfJS09LwP33pKSoErBMRquDdKKInIIXKuwIq2iBG6XJSqIfMjue-82NT87yq1bVzlQXfsNNbvsSiO1ElJ24EMPhtTknCi6barWPv05BLcvwGnj-gI69u4g9Tn4Oia_CVU-BgQapexeedNjFREdrwfHP3F6X9w</recordid><startdate>19940501</startdate><enddate>19940501</enddate><creator>Jenq-Tay Yuan</creator><creator>Stuller, J.A.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19940501</creationdate><title>Order-recursive FIR smoothers</title><author>Jenq-Tay Yuan ; Stuller, J.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c306t-c6b554702955eba32c3aa9e65062431f83781edef130c376cde76f5039b2160f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Adaptive filters</topic><topic>Applied sciences</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Estimation error</topic><topic>Exact sciences and technology</topic><topic>Filtering</topic><topic>Finite impulse response filter</topic><topic>Information, signal and communications theory</topic><topic>Kalman filters</topic><topic>Lattices</topic><topic>Maximum likelihood detection</topic><topic>Nonlinear filters</topic><topic>Parameter estimation</topic><topic>Signal and communications theory</topic><topic>Signal processing algorithms</topic><topic>Signal, noise</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jenq-Tay Yuan</creatorcontrib><creatorcontrib>Stuller, J.A.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jenq-Tay Yuan</au><au>Stuller, J.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Order-recursive FIR smoothers</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>1994-05-01</date><risdate>1994</risdate><volume>42</volume><issue>5</issue><spage>1242</spage><epage>1246</epage><pages>1242-1246</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>This paper introduces order-recursive FIR smoothers and shows that order-recursive FIR filters are special forms that occur when no future data values are used to estimate the signal. 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| fulltext | fulltext |
| identifier | ISSN: 1053-587X |
| ispartof | IEEE transactions on signal processing, 1994-05, Vol.42 (5), p.1242-1246 |
| issn | 1053-587X 1941-0476 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_4186695 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Adaptive filters Applied sciences Detection, estimation, filtering, equalization, prediction Estimation error Exact sciences and technology Filtering Finite impulse response filter Information, signal and communications theory Kalman filters Lattices Maximum likelihood detection Nonlinear filters Parameter estimation Signal and communications theory Signal processing algorithms Signal, noise Telecommunications and information theory |
| title | Order-recursive FIR smoothers |
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