Vector approximate message passing algorithm for compressed sensing with structured matrix perturbation
•Study the CS with structured matrix perturbation via Bayesian methods.•Develop the perturbation considered vector approximate message passing (PC-VAMP) algorithm.•Show the excellent performance of PC-VAMP. In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing...
Uloženo v:
| Vydáno v: | Signal processing Ročník 166; s. 107248 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.01.2020
|
| Témata: | |
| ISSN: | 0165-1684, 1872-7557 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •Study the CS with structured matrix perturbation via Bayesian methods.•Develop the perturbation considered vector approximate message passing (PC-VAMP) algorithm.•Show the excellent performance of PC-VAMP.
In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known. Such cases exist when there are imperfections or unknown calibration parameters during the measurement process. Particularly, the sensing matrix may have some structure, which makes the perturbation follow a fixed pattern. Previous work has focused on extending the approximate message passing (AMP) and LASSO algorithm to deal with the independent and identically distributed (i.i.d.) perturbation. Based on the recent VAMP algorithm, we take the structured perturbation into account and propose the perturbation considered vector approximate message passing (PC-VAMP) algorithm. Numerical results demonstrate the effectiveness of PC-VAMP. |
|---|---|
| AbstractList | •Study the CS with structured matrix perturbation via Bayesian methods.•Develop the perturbation considered vector approximate message passing (PC-VAMP) algorithm.•Show the excellent performance of PC-VAMP.
In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known. Such cases exist when there are imperfections or unknown calibration parameters during the measurement process. Particularly, the sensing matrix may have some structure, which makes the perturbation follow a fixed pattern. Previous work has focused on extending the approximate message passing (AMP) and LASSO algorithm to deal with the independent and identically distributed (i.i.d.) perturbation. Based on the recent VAMP algorithm, we take the structured perturbation into account and propose the perturbation considered vector approximate message passing (PC-VAMP) algorithm. Numerical results demonstrate the effectiveness of PC-VAMP. |
| ArticleNumber | 107248 |
| Author | Zhu, Jiang Zhang, Qi Meng, Xiangming Xu, Zhiwei |
| Author_xml | – sequence: 1 givenname: Jiang orcidid: 0000-0002-7646-2776 surname: Zhu fullname: Zhu, Jiang email: jiangzhu16@zju.edu.cn organization: Ocean College, Zhejiang University, Zhoushan, 316021, China – sequence: 2 givenname: Qi surname: Zhang fullname: Zhang, Qi email: zhangqi13@zju.edu.cn organization: Ocean College, Zhejiang University, Zhoushan, 316021, China – sequence: 3 givenname: Xiangming surname: Meng fullname: Meng, Xiangming organization: Huawei Technologies, Co. Ltd, Shanghai, 201206, China – sequence: 4 givenname: Zhiwei surname: Xu fullname: Xu, Zhiwei email: xuzw@zju.edu.cn organization: Ocean College, Zhejiang University, Zhoushan, 316021, China |
| BookMark | eNqFkMtOwzAQRS1UJNrCH7DwD6T4kTgJCyRU8ZIqsQG2luOMg6s2jmwXyt_jNqxYwMry-J4rz5mhSe96QOiSkgUlVFytF8F2g3cLRmidRiXLqxM0pVXJsrIoygmapliRUVHlZ2gWwpoQQrkgU9S9gY7OYzUkfm-3KgLeQgiqAzyoEGzfYbXpnLfxfYtNSmq3HXxKQIsD9MfAZ3rEIfqdjjuf5qnF2z0ewKd7o6J1_Tk6NWoT4OLnnKPX-7uX5WO2en54Wt6uMs2JiJmhuiFlIagAo5uGVC3VoFhbM5UDNLxsagbcCNJWOQPNuTa15pUwCvKK1YrPUT72au9C8GDk4NNW_ktSIg-y5FqOsuRBlhxlJez6F6ZtPH48emU3_8E3IwxpsQ8LXgZtodfQWp_sytbZvwu-AVTKjv0 |
| CitedBy_id | crossref_primary_10_1109_TSP_2020_3044847 crossref_primary_10_1016_j_dsp_2025_105410 crossref_primary_10_1016_j_sigpro_2020_107601 crossref_primary_10_1016_j_sigpro_2020_107711 |
| Cites_doi | 10.1109/TSP.2013.2272287 10.1109/TSP.2014.2330350 10.1109/TSP.2007.914323 10.1109/TSP.2011.2109956 10.1109/TSP.2012.2201152 10.1109/ISIT.2011.6033942 10.1109/ISIT.2017.8006797 10.1109/JSTSP.2009.2039170 10.1109/JSTSP.2011.2169232 10.1214/10-AOS793 10.1073/pnas.0909892106 10.1109/TSP.2012.2222377 10.1109/JSTSP.2014.2313766 10.1109/ICASSP.2017.7952957 10.1137/090756338 10.1109/LSP.2015.2391287 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier B.V. |
| Copyright_xml | – notice: 2019 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.sigpro.2019.107248 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1872-7557 |
| ExternalDocumentID | 10_1016_j_sigpro_2019_107248 S0165168419302944 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SEW SPC SPCBC SST SSV SSZ T5K TAE TN5 WUQ XPP ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c306t-f1cb075616efcbb08d1cea2d92a4eeb37b92e3f60d842ec33cf9c386fae4829a3 |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000491683100043&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0165-1684 |
| IngestDate | Sat Nov 29 07:26:49 EST 2025 Tue Nov 18 21:41:11 EST 2025 Fri Feb 23 02:33:59 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | VAMP Compressed sensing Structured perturbation |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-f1cb075616efcbb08d1cea2d92a4eeb37b92e3f60d842ec33cf9c386fae4829a3 |
| ORCID | 0000-0002-7646-2776 |
| ParticipantIDs | crossref_primary_10_1016_j_sigpro_2019_107248 crossref_citationtrail_10_1016_j_sigpro_2019_107248 elsevier_sciencedirect_doi_10_1016_j_sigpro_2019_107248 |
| PublicationCentury | 2000 |
| PublicationDate | January 2020 2020-01-00 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – month: 01 year: 2020 text: January 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Signal processing |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Ding, Chen, Gu (bib0005) 2013; 61 A.K. Fletcher, P. Schniter, Learning and free energies for vector approximate message passing, 2018, arXiv Zhu, Lin (bib0003) 2014 Yang, Zhang, Xie (bib0008) 2012; 60 Wu, Kuang, Ni, Lu, Huang, Guo, Sel (bib0014) 2014; 8 S. Rangan, Generalized approximate message passing for estimation with random linear mixing, 2012, arXiv Meng, Wu, Kuang, Lu (bib0015) 2015; 22 Zhu, Leus, Giannakis (bib0006) 2011; 59 Schniter (bib0011) 2011; 5 Hansen, Nagy, O’Leary (bib0021) 2006 Zhu, Wang, Lin, Gu (bib0002) 2014; 62 S. Rangan, P. Schniter, A. Fletcher, Vector approximate message passing, 2018, arXiv Herman, Strohmer (bib0004) 2010; 4 Krzakala, Mezard, Zdeborová (bib0020) 2013 Parker, Cevher, Schniter, CA (bib0019) 2011 . Minka (bib0013) 2001 Ma, Ping (bib0012) 2017; 5 Beck, Eldar (bib0022) 2010; 31 Vila, Schniter (bib0017) 2013; 61 Donoho, Maleki, Montanari (bib0009) 2009 Wiesel, Eldar, Yeredor (bib0001) 2008; 56 Rosenbaum, Tsybakov (bib0007) 2010; 38 Ma (10.1016/j.sigpro.2019.107248_bib0012) 2017; 5 Ding (10.1016/j.sigpro.2019.107248_bib0005) 2013; 61 10.1016/j.sigpro.2019.107248_bib0010 Parker (10.1016/j.sigpro.2019.107248_bib0019) 2011 Hansen (10.1016/j.sigpro.2019.107248_bib0021) 2006 Donoho (10.1016/j.sigpro.2019.107248_bib0009) 2009 Wu (10.1016/j.sigpro.2019.107248_bib0014) 2014; 8 Zhu (10.1016/j.sigpro.2019.107248_bib0002) 2014; 62 Minka (10.1016/j.sigpro.2019.107248_sbref0012) 2001 Yang (10.1016/j.sigpro.2019.107248_bib0008) 2012; 60 Wiesel (10.1016/j.sigpro.2019.107248_bib0001) 2008; 56 Zhu (10.1016/j.sigpro.2019.107248_bib0003) 2014 Herman (10.1016/j.sigpro.2019.107248_bib0004) 2010; 4 Rosenbaum (10.1016/j.sigpro.2019.107248_bib0007) 2010; 38 Beck (10.1016/j.sigpro.2019.107248_bib0022) 2010; 31 Schniter (10.1016/j.sigpro.2019.107248_bib0011) 2011; 5 10.1016/j.sigpro.2019.107248_bib0016 Meng (10.1016/j.sigpro.2019.107248_bib0015) 2015; 22 Vila (10.1016/j.sigpro.2019.107248_bib0017) 2013; 61 10.1016/j.sigpro.2019.107248_bib0018 Zhu (10.1016/j.sigpro.2019.107248_bib0006) 2011; 59 Krzakala (10.1016/j.sigpro.2019.107248_bib0020) 2013 |
| References_xml | – start-page: 18914 year: 2009 end-page: 18919 ident: bib0009 article-title: Message passing algorithms for compressed sensing publication-title: Proc. Natl. Acad. Sci. – volume: 56 start-page: 2194 year: 2008 end-page: 2205 ident: bib0001 article-title: Linear regression with gaussian model uncertainty: algorithms and bounds publication-title: IEEE Trans. Signal Process. – year: 2006 ident: bib0021 article-title: Deblurring Images: Matrices, Spectra, and Filtering – start-page: 48 year: 2014 end-page: 53 ident: bib0003 article-title: Sparse estimation from sign measurements with general sensing matrix perturbation publication-title: International Conference on Digital Signal Processing, Hong Kong – reference: S. Rangan, Generalized approximate message passing for estimation with random linear mixing, 2012, arXiv: – year: 2013 ident: bib0020 article-title: Compressed sensing under matrix uncertainty: optimum thresholds and robust approximate message passing publication-title: ICASSP – reference: . – volume: 22 start-page: 1194 year: 2015 end-page: 1197 ident: bib0015 article-title: An expectation propagation perspective on approximate message passing publication-title: IEEE Signal Process. Lett. – volume: 59 start-page: 2002 year: 2011 end-page: 2016 ident: bib0006 article-title: Sparsity-cognizant total leastsquares for perturbed compressive sampling publication-title: IEEE Trans. Signal Process. – volume: 31 start-page: 2623 year: 2010 end-page: 2649 ident: bib0022 article-title: Structured total maximum likelihood: an alternative to structured total least squares publication-title: SIAM J. Matrix Anal. Appl. – volume: 62 start-page: 3741 year: 2014 end-page: 3753 ident: bib0002 article-title: Maximum likelihood estimation from sign measurements with sensing matrix perturbation publication-title: IEEE Trans. Signal Process. – volume: 60 start-page: 4658 year: 2012 end-page: 4671 ident: bib0008 article-title: Robustly stable signal recovery in compressed sensing with structured matrix perturbation publication-title: IEEE Trans. Signal Process. – volume: 5 start-page: 1462 year: 2011 end-page: 1474 ident: bib0011 article-title: A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels publication-title: IEEE J. Sel. Top. Signal Process. – volume: 5 start-page: 5 year: 2017 end-page: 2033 ident: bib0012 article-title: Orthogonal AMP publication-title: IEEE Access – reference: S. Rangan, P. Schniter, A. Fletcher, Vector approximate message passing, 2018, arXiv: – volume: 61 start-page: 4658 year: 2013 end-page: 4672 ident: bib0017 article-title: Expectation-maximization Gaussian-mixture approximate message passing publication-title: IEEE Trans. Signal Process. – volume: 38 start-page: 2620 year: 2010 end-page: 2651 ident: bib0007 article-title: Sparse recovery under matrix uncertainty publication-title: Ann. Stat. – year: 2001 ident: bib0013 article-title: A family of algorithms for approximate Bayesian inference publication-title: Mass. Inst. Technol. – volume: 8 start-page: 902 year: 2014 end-page: 915 ident: bib0014 article-title: Low-complexity iterative detection for large-scale multiuser MIMO-OFDM systems using approximate message passing publication-title: IEEE. Top. Signal Process. – reference: A.K. Fletcher, P. Schniter, Learning and free energies for vector approximate message passing, 2018, arXiv: – volume: 61 start-page: 398 year: 2013 end-page: 410 ident: bib0005 article-title: Perturbation analysis of orthogonal matching pursuit publication-title: IEEE Trans. Signal Process. – volume: 4 start-page: 342 year: 2010 end-page: 349 ident: bib0004 article-title: General deviants: an analysis of perturbations in compressed sensing publication-title: IEEE J. Sel. Top. Signal Process. – start-page: 804 year: 2011 end-page: 808 ident: bib0019 article-title: Compressive sensing under matrix uncertainties: an approximate message passing approach publication-title: ASILOMAR – volume: 61 start-page: 4658 issue: 19 year: 2013 ident: 10.1016/j.sigpro.2019.107248_bib0017 article-title: Expectation-maximization Gaussian-mixture approximate message passing publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2013.2272287 – volume: 62 start-page: 3741 issue: 15 year: 2014 ident: 10.1016/j.sigpro.2019.107248_bib0002 article-title: Maximum likelihood estimation from sign measurements with sensing matrix perturbation publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2014.2330350 – year: 2006 ident: 10.1016/j.sigpro.2019.107248_bib0021 – start-page: 48 year: 2014 ident: 10.1016/j.sigpro.2019.107248_bib0003 article-title: Sparse estimation from sign measurements with general sensing matrix perturbation – volume: 56 start-page: 2194 issue: 6 year: 2008 ident: 10.1016/j.sigpro.2019.107248_bib0001 article-title: Linear regression with gaussian model uncertainty: algorithms and bounds publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2007.914323 – start-page: 804 year: 2011 ident: 10.1016/j.sigpro.2019.107248_bib0019 article-title: Compressive sensing under matrix uncertainties: an approximate message passing approach – volume: 59 start-page: 2002 issue: 5 year: 2011 ident: 10.1016/j.sigpro.2019.107248_bib0006 article-title: Sparsity-cognizant total leastsquares for perturbed compressive sampling publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2011.2109956 – volume: 60 start-page: 4658 issue: 9 year: 2012 ident: 10.1016/j.sigpro.2019.107248_bib0008 article-title: Robustly stable signal recovery in compressed sensing with structured matrix perturbation publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2012.2201152 – ident: 10.1016/j.sigpro.2019.107248_bib0010 doi: 10.1109/ISIT.2011.6033942 – ident: 10.1016/j.sigpro.2019.107248_bib0016 doi: 10.1109/ISIT.2017.8006797 – volume: 4 start-page: 342 issue: 2 year: 2010 ident: 10.1016/j.sigpro.2019.107248_bib0004 article-title: General deviants: an analysis of perturbations in compressed sensing publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2009.2039170 – volume: 5 start-page: 1462 issue: 8 year: 2011 ident: 10.1016/j.sigpro.2019.107248_bib0011 article-title: A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2011.2169232 – volume: 5 start-page: 5 year: 2017 ident: 10.1016/j.sigpro.2019.107248_bib0012 article-title: Orthogonal AMP publication-title: IEEE Access – volume: 38 start-page: 2620 issue: 5 year: 2010 ident: 10.1016/j.sigpro.2019.107248_bib0007 article-title: Sparse recovery under matrix uncertainty publication-title: Ann. Stat. doi: 10.1214/10-AOS793 – start-page: 18914 year: 2009 ident: 10.1016/j.sigpro.2019.107248_bib0009 article-title: Message passing algorithms for compressed sensing publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0909892106 – volume: 61 start-page: 398 issue: 2 year: 2013 ident: 10.1016/j.sigpro.2019.107248_bib0005 article-title: Perturbation analysis of orthogonal matching pursuit publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2012.2222377 – volume: 8 start-page: 902 issue: 5 year: 2014 ident: 10.1016/j.sigpro.2019.107248_bib0014 article-title: Low-complexity iterative detection for large-scale multiuser MIMO-OFDM systems using approximate message passing publication-title: IEEE. Top. Signal Process. doi: 10.1109/JSTSP.2014.2313766 – ident: 10.1016/j.sigpro.2019.107248_bib0018 doi: 10.1109/ICASSP.2017.7952957 – volume: 31 start-page: 2623 issue: 5 year: 2010 ident: 10.1016/j.sigpro.2019.107248_bib0022 article-title: Structured total maximum likelihood: an alternative to structured total least squares publication-title: SIAM J. Matrix Anal. Appl. doi: 10.1137/090756338 – year: 2001 ident: 10.1016/j.sigpro.2019.107248_sbref0012 article-title: A family of algorithms for approximate Bayesian inference – volume: 22 start-page: 1194 issue: 8 year: 2015 ident: 10.1016/j.sigpro.2019.107248_bib0015 article-title: An expectation propagation perspective on approximate message passing publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2015.2391287 – year: 2013 ident: 10.1016/j.sigpro.2019.107248_bib0020 article-title: Compressed sensing under matrix uncertainty: optimum thresholds and robust approximate message passing publication-title: ICASSP |
| SSID | ssj0001360 |
| Score | 2.3241322 |
| Snippet | •Study the CS with structured matrix perturbation via Bayesian methods.•Develop the perturbation considered vector approximate message passing (PC-VAMP)... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 107248 |
| SubjectTerms | Compressed sensing Structured perturbation VAMP |
| Title | Vector approximate message passing algorithm for compressed sensing with structured matrix perturbation |
| URI | https://dx.doi.org/10.1016/j.sigpro.2019.107248 |
| Volume | 166 |
| WOSCitedRecordID | wos000491683100043&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7557 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001360 issn: 0165-1684 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLdKxwEOiE9tfMkHbpWnxk6d-DihIUBoAjGmiEsUO06Wqc2qtRv583n-SGIYGuzAJWot20nzfvX7-em9nxF6A3-CuFBVTGKZKAIeuiRFpSVhfAFcrgIGbYW0Tz4lR0dplonPk0nX18JcLZO2TbtOrP-rqaENjG1KZ29h7mFSaIDPYHS4gtnh-k-GP7FxeCcW3jVASPVsZc45qfVsDUzZFiUu6_OLZnu6skmGJqvcSoiXs41JZ--js05a9tIkqK-MkH9nNI7huxyN6Vnt16Y2pHbtig56Z2jD0ZcWJoDBoM2HqL80g7W1a8lMv1UwPrPDv582P3QTxifoPIhP-JAlX5CIu4PghjWXh6smbEGp09u8tqC72MLZ_qap4UeYVDyxP3b_VT_7N782ZBv2iWxnuZslN7PkbpY7aIcmC5FO0c7Bh8Ps4-DFI2YrzIen78subW7g9af5M60JqMrxQ_TA7zHwgcPGIzTR7WN0P1CefIJqhxIcoAR7lGCPEjygBANK8IgS7FGCDUrwiBLsUIJDlDxF394dHr99T_yZG0TB5nFLqkhJYJE84rpSUs7TMlK6oKWgRay1ZIkUVLOKz8s0ploxpiqhWMqrQscpFQV7hqbteat3EY5ECXtrKWCHK2PBqVzQWM45uBAmSsnVHmL9K8uVF6Q356Is85sMtofIMGrtBFn-0j_prZF7UunIYg4Qu3Hk81ve6QW6N-L_JZrC69ev0F11tW02F689vn4CeL6guQ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Vector+approximate+message+passing+algorithm+for+compressed+sensing+with+structured+matrix+perturbation&rft.jtitle=Signal+processing&rft.au=Zhu%2C+Jiang&rft.au=Zhang%2C+Qi&rft.au=Meng%2C+Xiangming&rft.au=Xu%2C+Zhiwei&rft.date=2020-01-01&rft.issn=0165-1684&rft.volume=166&rft.spage=107248&rft_id=info:doi/10.1016%2Fj.sigpro.2019.107248&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_sigpro_2019_107248 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0165-1684&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0165-1684&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0165-1684&client=summon |