Blind calibration for compressed sensing: State evolution and an online algorithm

Compressed sensing, allows to acquire compressible signals with a small number of measurements. In applications, a hardware implementation often requires a calibration as the sensing process is not perfectly known. Blind calibration, that is performing at the same time calibration and compressed sen...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-03
Main Authors: Gabrié, Marylou, Barbier, Jean, Krzakala, Florent, Zdeborová, Lenka
Format: Article
Language:English
Subjects:
Calibration
Compressibility
Computer simulation
Detection
Evolutionary algorithms
Message passing
Time compression
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Gabrié, Marylou
Barbier, Jean
Krzakala, Florent
Zdeborová, Lenka
description Compressed sensing, allows to acquire compressible signals with a small number of measurements. In applications, a hardware implementation often requires a calibration as the sensing process is not perfectly known. Blind calibration, that is performing at the same time calibration and compressed sensing is thus particularly appealing. A potential approach was suggested by Sch\"ulke and collaborators in Sch\"ulke et al. 2013 and 2015, using approximate message passing (AMP) for blind calibration (cal-AMP). Here, the algorithm is extended from the already proposed offline case to the online case, where the calibration is refined step by step as new measured samples are received. Furthermore, we show that the performance of both the offline and the online algorithms can be theoretically studied via the State Evolution (SE) formalism. Through numerical simulations, the efficiency of cal-AMP and the consistency of the theoretical predictions are confirmed.
doi_str_mv 10.48550/arxiv.1910.00285
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2299947003</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2299947003</sourcerecordid><originalsourceid>FETCH-LOGICAL-a523-bcfc116575d626e6fbf7e603be8e41164a6dcf69608f702305ff055b239dd9b3</originalsourceid><addsrcrecordid>eNotjU9LwzAYxoMgOOY-gLeA5863SZM23nSoEwYi8z6S5s3M6JKZtMOPb1BPD_yef4Tc1LBsOiHgTqdvf17WqgAA1okLMmOc11XXMHZFFjkfoHDZMiH4jLw_Dj5Y2uvBm6RHHwN1MdE-Hk8Jc0ZLM4bsw_6ebkc9IsVzHKbfnC49HWgMZQGpHvYx-fHzeE0unR4yLv51TrbPTx-rdbV5e3ldPWwqLRivTO_6upaiFVYyidIZ16IEbrDDphiNlrZ3UknoXAuMg3AOhDCMK2uV4XNy-7d6SvFrwjzuDnFKoRzuGFNKNS0A5z9mEFEP</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2299947003</pqid></control><display><type>article</type><title>Blind calibration for compressed sensing: State evolution and an online algorithm</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><creator>Gabrié, Marylou ; Barbier, Jean ; Krzakala, Florent ; Zdeborová, Lenka</creator><creatorcontrib>Gabrié, Marylou ; Barbier, Jean ; Krzakala, Florent ; Zdeborová, Lenka</creatorcontrib><description>Compressed sensing, allows to acquire compressible signals with a small number of measurements. In applications, a hardware implementation often requires a calibration as the sensing process is not perfectly known. Blind calibration, that is performing at the same time calibration and compressed sensing is thus particularly appealing. A potential approach was suggested by Sch\"ulke and collaborators in Sch\"ulke et al. 2013 and 2015, using approximate message passing (AMP) for blind calibration (cal-AMP). Here, the algorithm is extended from the already proposed offline case to the online case, where the calibration is refined step by step as new measured samples are received. Furthermore, we show that the performance of both the offline and the online algorithms can be theoretically studied via the State Evolution (SE) formalism. Through numerical simulations, the efficiency of cal-AMP and the consistency of the theoretical predictions are confirmed.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1910.00285</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Calibration ; Compressibility ; Computer simulation ; Detection ; Evolutionary algorithms ; Message passing ; Time compression</subject><ispartof>arXiv.org, 2020-03</ispartof><rights>2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2299947003?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>776,780,25730,27901,36988,44565</link.rule.ids></links><search><creatorcontrib>Gabrié, Marylou</creatorcontrib><creatorcontrib>Barbier, Jean</creatorcontrib><creatorcontrib>Krzakala, Florent</creatorcontrib><creatorcontrib>Zdeborová, Lenka</creatorcontrib><title>Blind calibration for compressed sensing: State evolution and an online algorithm</title><title>arXiv.org</title><description>Compressed sensing, allows to acquire compressible signals with a small number of measurements. In applications, a hardware implementation often requires a calibration as the sensing process is not perfectly known. Blind calibration, that is performing at the same time calibration and compressed sensing is thus particularly appealing. A potential approach was suggested by Sch\"ulke and collaborators in Sch\"ulke et al. 2013 and 2015, using approximate message passing (AMP) for blind calibration (cal-AMP). Here, the algorithm is extended from the already proposed offline case to the online case, where the calibration is refined step by step as new measured samples are received. Furthermore, we show that the performance of both the offline and the online algorithms can be theoretically studied via the State Evolution (SE) formalism. Through numerical simulations, the efficiency of cal-AMP and the consistency of the theoretical predictions are confirmed.</description><subject>Calibration</subject><subject>Compressibility</subject><subject>Computer simulation</subject><subject>Detection</subject><subject>Evolutionary algorithms</subject><subject>Message passing</subject><subject>Time compression</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNotjU9LwzAYxoMgOOY-gLeA5863SZM23nSoEwYi8z6S5s3M6JKZtMOPb1BPD_yef4Tc1LBsOiHgTqdvf17WqgAA1okLMmOc11XXMHZFFjkfoHDZMiH4jLw_Dj5Y2uvBm6RHHwN1MdE-Hk8Jc0ZLM4bsw_6ebkc9IsVzHKbfnC49HWgMZQGpHvYx-fHzeE0unR4yLv51TrbPTx-rdbV5e3ldPWwqLRivTO_6upaiFVYyidIZ16IEbrDDphiNlrZ3UknoXAuMg3AOhDCMK2uV4XNy-7d6SvFrwjzuDnFKoRzuGFNKNS0A5z9mEFEP</recordid><startdate>20200323</startdate><enddate>20200323</enddate><creator>Gabrié, Marylou</creator><creator>Barbier, Jean</creator><creator>Krzakala, Florent</creator><creator>Zdeborová, Lenka</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200323</creationdate><title>Blind calibration for compressed sensing: State evolution and an online algorithm</title><author>Gabrié, Marylou ; Barbier, Jean ; Krzakala, Florent ; Zdeborová, Lenka</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a523-bcfc116575d626e6fbf7e603be8e41164a6dcf69608f702305ff055b239dd9b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Calibration</topic><topic>Compressibility</topic><topic>Computer simulation</topic><topic>Detection</topic><topic>Evolutionary algorithms</topic><topic>Message passing</topic><topic>Time compression</topic><toplevel>online_resources</toplevel><creatorcontrib>Gabrié, Marylou</creatorcontrib><creatorcontrib>Barbier, Jean</creatorcontrib><creatorcontrib>Krzakala, Florent</creatorcontrib><creatorcontrib>Zdeborová, Lenka</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied &amp; Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gabrié, Marylou</au><au>Barbier, Jean</au><au>Krzakala, Florent</au><au>Zdeborová, Lenka</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Blind calibration for compressed sensing: State evolution and an online algorithm</atitle><jtitle>arXiv.org</jtitle><date>2020-03-23</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>Compressed sensing, allows to acquire compressible signals with a small number of measurements. In applications, a hardware implementation often requires a calibration as the sensing process is not perfectly known. Blind calibration, that is performing at the same time calibration and compressed sensing is thus particularly appealing. A potential approach was suggested by Sch\"ulke and collaborators in Sch\"ulke et al. 2013 and 2015, using approximate message passing (AMP) for blind calibration (cal-AMP). Here, the algorithm is extended from the already proposed offline case to the online case, where the calibration is refined step by step as new measured samples are received. Furthermore, we show that the performance of both the offline and the online algorithms can be theoretically studied via the State Evolution (SE) formalism. Through numerical simulations, the efficiency of cal-AMP and the consistency of the theoretical predictions are confirmed.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.1910.00285</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2020-03
issn 2331-8422
language eng
recordid cdi_proquest_journals_2299947003
source Publicly Available Content Database (Proquest) (PQ_SDU_P3)
subjects Calibration
Compressibility
Computer simulation
Detection
Evolutionary algorithms
Message passing
Time compression
title Blind calibration for compressed sensing: State evolution and an online algorithm
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-24T17%3A27%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Blind%20calibration%20for%20compressed%20sensing:%20State%20evolution%20and%20an%20online%20algorithm&rft.jtitle=arXiv.org&rft.au=Gabri%C3%A9,%20Marylou&rft.date=2020-03-23&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.1910.00285&rft_dat=%3Cproquest%3E2299947003%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a523-bcfc116575d626e6fbf7e603be8e41164a6dcf69608f702305ff055b239dd9b3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2299947003&rft_id=info:pmid/&rfr_iscdi=true