A New State-Regularized QRRLS Algorithm With a Variable Forgetting Factor
This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD) recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor (VFF). It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, w...
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| Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2012-03, Vol.59 (3), p.183-187 |
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| container_title | IEEE transactions on circuits and systems. II, Express briefs |
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| creator | Chan, S. C. Chu, Y. J. |
| description | This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD) recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor (VFF). It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance over a conventional RLS algorithm and reduced bias over an L 2 -regularized RLS algorithm. To improve the tracking performance, a new measure of convergence status is introduced in controlling the forgetting factor. Consequently, the resultant SR-VFF-RLS algorithm stabilizes the update and adaptively selects the number of measurements by means of the VFF. Improved tracking performance, steady-state mean-square error, and robustness to power-varying inputs over conventional RLS algorithms can be achieved. Furthermore, the proposed algorithm can be implemented using QRD, which leads to a lower roundoff error and more efficient hardware realization than the direct implementation. The effectiveness of the proposed algorithm is demonstrated by computer simulations. |
| doi_str_mv | 10.1109/TCSII.2012.2184374 |
| format | Article |
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C. ; Chu, Y. J.</creator><creatorcontrib>Chan, S. C. ; Chu, Y. J.</creatorcontrib><description>This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD) recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor (VFF). It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance over a conventional RLS algorithm and reduced bias over an L 2 -regularized RLS algorithm. To improve the tracking performance, a new measure of convergence status is introduced in controlling the forgetting factor. Consequently, the resultant SR-VFF-RLS algorithm stabilizes the update and adaptively selects the number of measurements by means of the VFF. Improved tracking performance, steady-state mean-square error, and robustness to power-varying inputs over conventional RLS algorithms can be achieved. Furthermore, the proposed algorithm can be implemented using QRD, which leads to a lower roundoff error and more efficient hardware realization than the direct implementation. The effectiveness of the proposed algorithm is demonstrated by computer simulations.</description><identifier>ISSN: 1549-7747</identifier><identifier>EISSN: 1558-3791</identifier><identifier>DOI: 10.1109/TCSII.2012.2184374</identifier><identifier>CODEN: ICSPE5</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive filters ; Algorithms ; Convergence ; Errors ; Hardware ; Least squares approximation ; Performance enhancement ; QR decomposition (QRD) ; recursive least squares (RLS) ; Robustness ; Signal processing algorithms ; Signal to noise ratio ; Steady-state ; Studies ; Tracking ; variable forgetting factor (VFF) ; variable regularization ; Vectors</subject><ispartof>IEEE transactions on circuits and systems. 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J.</creatorcontrib><title>A New State-Regularized QRRLS Algorithm With a Variable Forgetting Factor</title><title>IEEE transactions on circuits and systems. II, Express briefs</title><addtitle>TCSII</addtitle><description>This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD) recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor (VFF). It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance over a conventional RLS algorithm and reduced bias over an L 2 -regularized RLS algorithm. To improve the tracking performance, a new measure of convergence status is introduced in controlling the forgetting factor. Consequently, the resultant SR-VFF-RLS algorithm stabilizes the update and adaptively selects the number of measurements by means of the VFF. Improved tracking performance, steady-state mean-square error, and robustness to power-varying inputs over conventional RLS algorithms can be achieved. Furthermore, the proposed algorithm can be implemented using QRD, which leads to a lower roundoff error and more efficient hardware realization than the direct implementation. The effectiveness of the proposed algorithm is demonstrated by computer simulations.</description><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Convergence</subject><subject>Errors</subject><subject>Hardware</subject><subject>Least squares approximation</subject><subject>Performance enhancement</subject><subject>QR decomposition (QRD)</subject><subject>recursive least squares (RLS)</subject><subject>Robustness</subject><subject>Signal processing algorithms</subject><subject>Signal to noise ratio</subject><subject>Steady-state</subject><subject>Studies</subject><subject>Tracking</subject><subject>variable forgetting factor (VFF)</subject><subject>variable regularization</subject><subject>Vectors</subject><issn>1549-7747</issn><issn>1558-3791</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1Lw0AQhoMoWKt_QC-LJy-pO_uZPZZiNVAU26rHZZNMYkra1E2C6K83tcWDl5mBed5heILgEugIgJrb5WQRxyNGgY0YRIJrcRQMQMoo5NrA8W4WJtRa6NPgrGlWlDJDORsE8Zg84idZtK7FcI5FVzlffmNGnufz2YKMq6L2Zfu-Jm99JY689muXVEimtS-wbctNQaYubWt_Hpzkrmrw4tCHwcv0bjl5CGdP9_FkPAtTznQbCpdrNFQmRkrJo5QpkUsBGaiIJizFVDHQkWAMmOFURVmuE2OiJEtBGsWRD4Ob_d2trz86bFq7LpsUq8ptsO4aC0qD0BEF6NHrf-iq7vym_84aZoAbpngPsT2U-rppPOZ268u1818WqN3Jtb9y7U6uPcjtQ1f7UImIfwEFQiom-Q_c03Kv</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>Chan, S. 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J.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chan, S. C.</au><au>Chu, Y. J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A New State-Regularized QRRLS Algorithm With a Variable Forgetting Factor</atitle><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle><stitle>TCSII</stitle><date>2012-03</date><risdate>2012</risdate><volume>59</volume><issue>3</issue><spage>183</spage><epage>187</epage><pages>183-187</pages><issn>1549-7747</issn><eissn>1558-3791</eissn><coden>ICSPE5</coden><abstract>This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD) recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor (VFF). It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance over a conventional RLS algorithm and reduced bias over an L 2 -regularized RLS algorithm. To improve the tracking performance, a new measure of convergence status is introduced in controlling the forgetting factor. Consequently, the resultant SR-VFF-RLS algorithm stabilizes the update and adaptively selects the number of measurements by means of the VFF. Improved tracking performance, steady-state mean-square error, and robustness to power-varying inputs over conventional RLS algorithms can be achieved. Furthermore, the proposed algorithm can be implemented using QRD, which leads to a lower roundoff error and more efficient hardware realization than the direct implementation. The effectiveness of the proposed algorithm is demonstrated by computer simulations.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSII.2012.2184374</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 1549-7747 |
| ispartof | IEEE transactions on circuits and systems. II, Express briefs, 2012-03, Vol.59 (3), p.183-187 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive filters Algorithms Convergence Errors Hardware Least squares approximation Performance enhancement QR decomposition (QRD) recursive least squares (RLS) Robustness Signal processing algorithms Signal to noise ratio Steady-state Studies Tracking variable forgetting factor (VFF) variable regularization Vectors |
| title | A New State-Regularized QRRLS Algorithm With a Variable Forgetting Factor |
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