A fast least-squares algorithm for linearly constrained adaptive filtering
An extension of the field of fast least-squares techniques is presented. It is shown that the adaptation gain, which is updated with a number of operations proportional to the number of transversal filter coefficients, can be used to update the coefficients of a linearly constrained adaptive filter....
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| Veröffentlicht in: | IEEE transactions on signal processing 1996-05, Vol.44 (5), p.1168-1174 |
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| container_end_page | 1174 |
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| container_issue | 5 |
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| container_title | IEEE transactions on signal processing |
| container_volume | 44 |
| creator | Resende, L.S. Romano, J.M.T. Bellanger, M.G. |
| description | An extension of the field of fast least-squares techniques is presented. It is shown that the adaptation gain, which is updated with a number of operations proportional to the number of transversal filter coefficients, can be used to update the coefficients of a linearly constrained adaptive filter. An algorithm that is robust to round-off errors is derived. It is general and flexible. It can handle multiple constraints and multichannel signals. Its performance is illustrated by simulations and compared with the classical LMS-based Frost (1972) algorithm. |
| doi_str_mv | 10.1109/78.502329 |
| format | Article |
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It is shown that the adaptation gain, which is updated with a number of operations proportional to the number of transversal filter coefficients, can be used to update the coefficients of a linearly constrained adaptive filter. An algorithm that is robust to round-off errors is derived. It is general and flexible. It can handle multiple constraints and multichannel signals. Its performance is illustrated by simulations and compared with the classical LMS-based Frost (1972) algorithm.</description><subject>Adaptive arrays</subject><subject>Adaptive filters</subject><subject>Applied sciences</subject><subject>Array signal processing</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Filtering algorithms</subject><subject>Information, signal and communications theory</subject><subject>Robustness</subject><subject>Roundoff errors</subject><subject>Signal analysis</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Telecommunications and information theory</subject><subject>Transversal filters</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><recordid>eNqFkM1LAzEQxYMoWKsHr55yEMHD1nxsNsmxiJ8UvCh4W2azSY2ku22yFfrfG9nSq5d5M8xvHsxD6JKSGaVE30k1E4Rxpo_QhOqSFqSU1XHuieCFUPLzFJ2l9E0ILUtdTdDrHDtIAw421yJtthBtwhCWffTD1wq7PuLgOwsx7LDpuzREyGOLoYX14H8sdj4MNvpueY5OHIRkL_Y6RR-PD-_3z8Xi7enlfr4oDOdyKEAQ6YSpJNVaN0y3QrqWNQDMlbzhgjdZyqappBaEWyUrMFxZYyvFHCGGT9HN6LuO_WZr01CvfDI2BOhsv001U5wRmf_9F6wo1apkGbwdQRP7lKJ19Tr6FcRdTUn9F2stVT3GmtnrvSkkA8FF6IxPhwNOtORMZuxqxLy19rDde_wCVZV_Hw</recordid><startdate>19960501</startdate><enddate>19960501</enddate><creator>Resende, L.S.</creator><creator>Romano, J.M.T.</creator><creator>Bellanger, M.G.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>7SC</scope><scope>JQ2</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19960501</creationdate><title>A fast least-squares algorithm for linearly constrained adaptive filtering</title><author>Resende, L.S. ; Romano, J.M.T. ; Bellanger, M.G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-a507f5c671999b29d57fd2baa2f43b353b43b4bb679503e876ac38ece682f00c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Adaptive arrays</topic><topic>Adaptive filters</topic><topic>Applied sciences</topic><topic>Array signal processing</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Exact sciences and technology</topic><topic>Filtering algorithms</topic><topic>Information, signal and communications theory</topic><topic>Robustness</topic><topic>Roundoff errors</topic><topic>Signal analysis</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal, noise</topic><topic>Telecommunications and information theory</topic><topic>Transversal filters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Resende, L.S.</creatorcontrib><creatorcontrib>Romano, J.M.T.</creatorcontrib><creatorcontrib>Bellanger, M.G.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Computer Science Collection</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_linktorsrc</fulltext></delivery><addata><au>Resende, L.S.</au><au>Romano, J.M.T.</au><au>Bellanger, M.G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A fast least-squares algorithm for linearly constrained adaptive filtering</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>1996-05-01</date><risdate>1996</risdate><volume>44</volume><issue>5</issue><spage>1168</spage><epage>1174</epage><pages>1168-1174</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>An extension of the field of fast least-squares techniques is presented. It is shown that the adaptation gain, which is updated with a number of operations proportional to the number of transversal filter coefficients, can be used to update the coefficients of a linearly constrained adaptive filter. An algorithm that is robust to round-off errors is derived. It is general and flexible. It can handle multiple constraints and multichannel signals. Its performance is illustrated by simulations and compared with the classical LMS-based Frost (1972) algorithm.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/78.502329</doi><tpages>7</tpages></addata></record> |
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| identifier | ISSN: 1053-587X |
| ispartof | IEEE transactions on signal processing, 1996-05, Vol.44 (5), p.1168-1174 |
| issn | 1053-587X 1941-0476 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_28320705 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive arrays Adaptive filters Applied sciences Array signal processing Detection, estimation, filtering, equalization, prediction Exact sciences and technology Filtering algorithms Information, signal and communications theory Robustness Roundoff errors Signal analysis Signal and communications theory Signal processing Signal, noise Telecommunications and information theory Transversal filters |
| title | A fast least-squares algorithm for linearly constrained adaptive filtering |
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