A New Transform-Domain Regularized Recursive Least M-Estimate Algorithm for a Robust Linear Estimation
This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR -decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimat...
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| Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2011-02, Vol.58 (2), p.120-124 |
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| container_title | IEEE transactions on circuits and systems. II, Express briefs |
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| creator | Chan, S C Zhang, Z G Chu, Y J |
| description | This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR -decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR -based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated. |
| doi_str_mv | 10.1109/TCSII.2011.2106314 |
| format | Article |
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By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR -based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated.</description><identifier>ISSN: 1549-7747</identifier><identifier>EISSN: 1558-3791</identifier><identifier>DOI: 10.1109/TCSII.2011.2106314</identifier><identifier>CODEN: ICSPE5</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Complexity theory ; Correlation ; Deviation ; Estimation ; Least squares method ; Mean square errors ; Noise ; QR decomposition (QRD) ; Recursive ; recursive linear estimation and filtering ; Regression coefficients ; Regularization ; Robustness ; Signal processing algorithms ; Simulation ; smoothly clipped absolute deviation (SCAD) ; Studies ; system identification ; transformed M-estimation (ME)</subject><ispartof>IEEE transactions on circuits and systems. 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II, Express briefs</title><addtitle>TCSII</addtitle><description>This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR -decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR -based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Complexity theory</subject><subject>Correlation</subject><subject>Deviation</subject><subject>Estimation</subject><subject>Least squares method</subject><subject>Mean square errors</subject><subject>Noise</subject><subject>QR decomposition (QRD)</subject><subject>Recursive</subject><subject>recursive linear estimation and filtering</subject><subject>Regression coefficients</subject><subject>Regularization</subject><subject>Robustness</subject><subject>Signal processing algorithms</subject><subject>Simulation</subject><subject>smoothly clipped absolute deviation (SCAD)</subject><subject>Studies</subject><subject>system identification</subject><subject>transformed M-estimation (ME)</subject><issn>1549-7747</issn><issn>1558-3791</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkEtPwzAQhCMEEuXxB-BiceKS4rVjOzlW5VWpgATlbDnJBlwlMdgJCH49Ca04cFjtHL5Z7UwUnQCdAtDsYjV_WiymjAJMGVDJIdmJJiBEGnOVwe6okyxWKlH70UEIa0pZRjmbRNWM3OMnWXnThsr5Jr50jbEtecSXvjbefmM56KL3wX4gWaIJHbmLr0JnG9MhmdUvztvutSGDmRjy6PJ-IJa2RePJFrOuPYr2KlMHPN7uw-j5-mo1v42XDzeL-WwZF5ypLmaCIlDGTMZSIUVeKEh5UeSQVFBxk7JS0pLmeT4kLZNclFQZSEGhRMHHOYzON3ffvHvvMXS6saHAujYtuj5okApYIhMhB_TsH7p2vW-H73QqeCol5SPENlDhXQgeK_3mh0j-SwPVY_P6t3k9Nq-3zQ-m043JIuKfQSjIQAL_AUOBfok</recordid><startdate>201102</startdate><enddate>201102</enddate><creator>Chan, S C</creator><creator>Zhang, Z G</creator><creator>Chu, Y J</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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II, Express briefs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chan, S C</au><au>Zhang, Z G</au><au>Chu, Y J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A New Transform-Domain Regularized Recursive Least M-Estimate Algorithm for a Robust Linear Estimation</atitle><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle><stitle>TCSII</stitle><date>2011-02</date><risdate>2011</risdate><volume>58</volume><issue>2</issue><spage>120</spage><epage>124</epage><pages>120-124</pages><issn>1549-7747</issn><eissn>1558-3791</eissn><coden>ICSPE5</coden><abstract>This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR -decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR -based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSII.2011.2106314</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 1549-7747 |
| ispartof | IEEE transactions on circuits and systems. II, Express briefs, 2011-02, Vol.58 (2), p.120-124 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Complexity theory Correlation Deviation Estimation Least squares method Mean square errors Noise QR decomposition (QRD) Recursive recursive linear estimation and filtering Regression coefficients Regularization Robustness Signal processing algorithms Simulation smoothly clipped absolute deviation (SCAD) Studies system identification transformed M-estimation (ME) |
| title | A New Transform-Domain Regularized Recursive Least M-Estimate Algorithm for a Robust Linear Estimation |
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