Robust Sparse Signal Recovery in the Presence of the S \alphaS Noise
In this letter, robust sparse signal recovery is considered in the presence of the symmetric α-stable distributed noise. An M-estimate type model is constructed by approximating the location score function of the noise. A reweighed iterative hard thresholding algorithm is proposed to recover the spa...
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| Veröffentlicht in: | IEEE signal processing letters 2016-11, Vol.23 (11), p.1687-1691 |
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| container_end_page | 1691 |
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| container_issue | 11 |
| container_start_page | 1687 |
| container_title | IEEE signal processing letters |
| container_volume | 23 |
| creator | Hu, Rui Fu, Yuli Chen, Zhen Xiang, Youjun Rong, Rong |
| description | In this letter, robust sparse signal recovery is considered in the presence of the symmetric α-stable distributed noise. An M-estimate type model is constructed by approximating the location score function of the noise. A reweighed iterative hard thresholding algorithm is proposed to recover the sparse signal. The basis functions for the approximation and the recovery performance of the proposed algorithm are discussed. Simulations are given to demonstrate the validity of our results. |
| doi_str_mv | 10.1109/LSP.2016.2614983 |
| format | Article |
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Simulations are given to demonstrate the validity of our results.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2016.2614983</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>IEEE</publisher><subject>Approximation algorithms ; Iterative algorithms ; Iterative hard thresholding (IHT) ; Linear programming ; M-estimate ; Noise measurement ; Sensors ; Signal processing algorithms ; Sparse matrices ; sparse signal recovery ; symmetric alpha -stable (S alphaS) noise</subject><ispartof>IEEE signal processing letters, 2016-11, Vol.23 (11), p.1687-1691</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7582482$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27922,27923,54756</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7582482$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hu, Rui</creatorcontrib><creatorcontrib>Fu, Yuli</creatorcontrib><creatorcontrib>Chen, Zhen</creatorcontrib><creatorcontrib>Xiang, Youjun</creatorcontrib><creatorcontrib>Rong, Rong</creatorcontrib><title>Robust Sparse Signal Recovery in the Presence of the S \alphaS Noise</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>In this letter, robust sparse signal recovery is considered in the presence of the symmetric α-stable distributed noise. 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Simulations are given to demonstrate the validity of our results.</description><subject>Approximation algorithms</subject><subject>Iterative algorithms</subject><subject>Iterative hard thresholding (IHT)</subject><subject>Linear programming</subject><subject>M-estimate</subject><subject>Noise measurement</subject><subject>Sensors</subject><subject>Signal processing algorithms</subject><subject>Sparse matrices</subject><subject>sparse signal recovery</subject><subject>symmetric alpha -stable (S alphaS) noise</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9ickKwjAUAIMoWJe74OX9QOtLupieXfAgUhqPgkR51UhtS1IF_14Rz55mhmFswjHgHNPZVmWBQJ4EIuFRKsMO83gcS1-ECe9-HOfopynKPhs4d0NEyWXssWVenx6uBdVo6wiUuVS6hJzO9ZPsC0wF7ZUgs-SoOhPUxbcVHHTZXLWCXW0cjViv0KWj8Y9DNl2v9ouNb4jo2Fhz1_Z1nMdSRFKE_-8bAYA6Bw</recordid><startdate>201611</startdate><enddate>201611</enddate><creator>Hu, Rui</creator><creator>Fu, Yuli</creator><creator>Chen, Zhen</creator><creator>Xiang, Youjun</creator><creator>Rong, Rong</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope></search><sort><creationdate>201611</creationdate><title>Robust Sparse Signal Recovery in the Presence of the S \alphaS Noise</title><author>Hu, Rui ; Fu, Yuli ; Chen, Zhen ; Xiang, Youjun ; Rong, Rong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_75824823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Approximation algorithms</topic><topic>Iterative algorithms</topic><topic>Iterative hard thresholding (IHT)</topic><topic>Linear programming</topic><topic>M-estimate</topic><topic>Noise measurement</topic><topic>Sensors</topic><topic>Signal processing algorithms</topic><topic>Sparse matrices</topic><topic>sparse signal recovery</topic><topic>symmetric alpha -stable (S alphaS) noise</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Rui</creatorcontrib><creatorcontrib>Fu, Yuli</creatorcontrib><creatorcontrib>Chen, Zhen</creatorcontrib><creatorcontrib>Xiang, Youjun</creatorcontrib><creatorcontrib>Rong, Rong</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><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hu, Rui</au><au>Fu, Yuli</au><au>Chen, Zhen</au><au>Xiang, Youjun</au><au>Rong, Rong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust Sparse Signal Recovery in the Presence of the S \alphaS Noise</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2016-11</date><risdate>2016</risdate><volume>23</volume><issue>11</issue><spage>1687</spage><epage>1691</epage><pages>1687-1691</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>In this letter, robust sparse signal recovery is considered in the presence of the symmetric α-stable distributed noise. An M-estimate type model is constructed by approximating the location score function of the noise. A reweighed iterative hard thresholding algorithm is proposed to recover the sparse signal. The basis functions for the approximation and the recovery performance of the proposed algorithm are discussed. Simulations are given to demonstrate the validity of our results.</abstract><pub>IEEE</pub><doi>10.1109/LSP.2016.2614983</doi></addata></record> |
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| subjects | Approximation algorithms Iterative algorithms Iterative hard thresholding (IHT) Linear programming M-estimate Noise measurement Sensors Signal processing algorithms Sparse matrices sparse signal recovery symmetric alpha -stable (S alphaS) noise |
| title | Robust Sparse Signal Recovery in the Presence of the S \alphaS Noise |
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