An Efficient Line-Search Algorithm for Unbiased Recursive Least-Squares Filtering With Noisy Inputs
This letter proposes a new algorithm for efficiently finding an unbiased RLS estimate of FIR models with noisy inputs. The unbiased estimate is obtained without knowing any a priori information via a new cost. Furthermore, to reduce computational complexity, the estimate is updated along the current...
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| Veröffentlicht in: | IEEE signal processing letters 2013-07, Vol.20 (7), p.693-696 |
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| container_title | IEEE signal processing letters |
| container_volume | 20 |
| creator | ByungHoon Kang PooGyeon Park |
| description | This letter proposes a new algorithm for efficiently finding an unbiased RLS estimate of FIR models with noisy inputs. The unbiased estimate is obtained without knowing any a priori information via a new cost. Furthermore, to reduce computational complexity, the estimate is updated along the current input-vector direction and the corresponding gain is efficiently computed. In addition, to increase the convergence rate, the algorithm is extended to update the estimate along not only current but also past input-vector directions. Simulation results show that the proposed algorithm exhibits a fast convergence rate and an enhanced tracking performance with noisy correlated inputs. |
| doi_str_mv | 10.1109/LSP.2013.2263134 |
| format | Article |
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The unbiased estimate is obtained without knowing any a priori information via a new cost. Furthermore, to reduce computational complexity, the estimate is updated along the current input-vector direction and the corresponding gain is efficiently computed. In addition, to increase the convergence rate, the algorithm is extended to update the estimate along not only current but also past input-vector directions. Simulation results show that the proposed algorithm exhibits a fast convergence rate and an enhanced tracking performance with noisy correlated inputs.</description><subject>Approximation algorithms</subject><subject>Bias-compensated LS</subject><subject>Computational complexity</subject><subject>Convergence</subject><subject>Finite impulse response filters</subject><subject>Noise measurement</subject><subject>noisy FIR model</subject><subject>Signal processing algorithms</subject><subject>total least-squares</subject><subject>Vectors</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFKAzEURYMoWKt7wU1-IPUlmSQzy1KqFgYVa3E5pJmXNtLO1GQq9O-d0uLqvsU9j8sh5J7DiHMoHsv5-0gAlyMhtOQyuyADrlTOhNT8sr_BACsKyK_JTUrfAJDzXA2IGzd06n1wAZuOlqFBNkcb3ZqON6s2hm69pb6NdNEsg01Y0w90-5jCL9ISberY_GdvIyb6FDYdxtCs6FcP0dc2pAOdNbt9l27JlbebhHfnHJLF0_Rz8sLKt-fZZFwy10_uWGYLgcJo752TRmZKgEfLuZHK6iWXzma8dhoK04czzqC3tba1cqpGKQo5JHD662KbUkRf7WLY2nioOFRHSVUvqTpKqs6SeuThhARE_K9rxTWAln-PAGRb</recordid><startdate>20130701</startdate><enddate>20130701</enddate><creator>ByungHoon Kang</creator><creator>PooGyeon Park</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20130701</creationdate><title>An Efficient Line-Search Algorithm for Unbiased Recursive Least-Squares Filtering With Noisy Inputs</title><author>ByungHoon Kang ; PooGyeon Park</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c263t-4a92e276ffcc3734520fea11735a6b13ca41dc60971dcc7c7efad6ad5c5de3293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Approximation algorithms</topic><topic>Bias-compensated LS</topic><topic>Computational complexity</topic><topic>Convergence</topic><topic>Finite impulse response filters</topic><topic>Noise measurement</topic><topic>noisy FIR model</topic><topic>Signal processing algorithms</topic><topic>total least-squares</topic><topic>Vectors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>ByungHoon Kang</creatorcontrib><creatorcontrib>PooGyeon Park</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><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ByungHoon Kang</au><au>PooGyeon Park</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Efficient Line-Search Algorithm for Unbiased Recursive Least-Squares Filtering With Noisy Inputs</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2013-07-01</date><risdate>2013</risdate><volume>20</volume><issue>7</issue><spage>693</spage><epage>696</epage><pages>693-696</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>This letter proposes a new algorithm for efficiently finding an unbiased RLS estimate of FIR models with noisy inputs. The unbiased estimate is obtained without knowing any a priori information via a new cost. Furthermore, to reduce computational complexity, the estimate is updated along the current input-vector direction and the corresponding gain is efficiently computed. In addition, to increase the convergence rate, the algorithm is extended to update the estimate along not only current but also past input-vector directions. Simulation results show that the proposed algorithm exhibits a fast convergence rate and an enhanced tracking performance with noisy correlated inputs.</abstract><pub>IEEE</pub><doi>10.1109/LSP.2013.2263134</doi><tpages>4</tpages></addata></record> |
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| ispartof | IEEE signal processing letters, 2013-07, Vol.20 (7), p.693-696 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Approximation algorithms Bias-compensated LS Computational complexity Convergence Finite impulse response filters Noise measurement noisy FIR model Signal processing algorithms total least-squares Vectors |
| title | An Efficient Line-Search Algorithm for Unbiased Recursive Least-Squares Filtering With Noisy Inputs |
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