Sparse Reconstruction for Radar Imaging Based on Quantum Algorithms

The sparse-driven radar imaging can obtain high-resolution images about a target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In t...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE geoscience and remote sensing letters Ročník 19; s. 1 - 5
Hlavní autoři: Liu, Xiaowen, Dong, Chen, Luo, Ying, Kang, Le, Liu, Yong, Zhang, Qun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Circuit design
Complexity
Compressive sensing (CS)
Computational complexity
Computer applications
Image reconstruction
Image resolution
Imaging
Imaging techniques
Logic gates
Mathematical model
quantum algorithm
Quantum circuit
Radar
Radar data
Radar imaging
Recovery
Registers
sparse recovery
ISSN:1545-598X, 1558-0571
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract The sparse-driven radar imaging can obtain high-resolution images about a target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this letter, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. First, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method.
AbstractList The sparse-driven radar imaging can obtain high-resolution images about a target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this letter, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. First, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method.
Author Luo, Ying
Liu, Yong
Liu, Xiaowen
Kang, Le
Zhang, Qun
Dong, Chen
Author_xml – sequence: 1
  givenname: Xiaowen
  orcidid: 0000-0002-4870-1522
  surname: Liu
  fullname: Liu, Xiaowen
  email: lxw5054@163.com
  organization: College of Information and Communication, National University of Defense Technology, Xi'an, China
– sequence: 2
  givenname: Chen
  surname: Dong
  fullname: Dong, Chen
  email: dongchengfkd@163.com
  organization: College of Information and Communication, National University of Defense Technology, Xi'an, China
– sequence: 3
  givenname: Ying
  orcidid: 0000-0003-1460-4289
  surname: Luo
  fullname: Luo, Ying
  email: luoying2002521@163.com
  organization: Institute of Information and Navigation, Air Force Engineering University, Xi'an, China
– sequence: 4
  givenname: Le
  orcidid: 0000-0001-8484-8794
  surname: Kang
  fullname: Kang, Le
  email: 18810495946@163.com
  organization: Institute of Information and Navigation, Air Force Engineering University, Xi'an, China
– sequence: 5
  givenname: Yong
  surname: Liu
  fullname: Liu, Yong
  email: yongliu@quanta.org.cn
  organization: College of Information and Communication, National University of Defense Technology, Xi'an, China
– sequence: 6
  givenname: Qun
  orcidid: 0000-0002-2773-3437
  surname: Zhang
  fullname: Zhang, Qun
  email: zhangqunnus@gmail.com
  organization: Institute of Information and Navigation, Air Force Engineering University, Xi'an, China
BookMark eNp9kE1LAzEQhoMo2FZ_gHhZ8Lx1kmy-jrVoLRTEVsFbyKbZuqXd1CR78N-7S4sHD55mYN5nhnmG6LzxjUPoBsMYY1D3i9lyNSZA8JhiKICoMzTAjMkcmMDnfV-wnCn5cYmGMW4BSCGlGKDp6mBCdNnSWd_EFFqbat9klQ_Z0qxNyOZ7s6mbTfZgoltn3ei1NU1q99lkt_GhTp_7eIUuKrOL7vpUR-j96fFt-pwvXmbz6WSRW6JoyitOuCihKqx0sgIuGWGl5OAENiU3FCvJK8qEKKVwgEuuLKxJoUrDiQELdITujnsPwX-1Lia99W1oupOa8E6CAkxZlxLHlA0-xuAqbetk-q9SMPVOY9C9Md0b070xfTLWkfgPeQj13oTvf5nbI1M7537zimFBMac_n2l3iA
CODEN IGRSBY
CitedBy_id crossref_primary_10_1109_ACCESS_2025_3531407
crossref_primary_10_1080_01431161_2024_2339204
crossref_primary_10_1109_LGRS_2024_3517135
Cites_doi 10.1109/TCI.2017.2750330
10.1109/JSTARS.2013.2263309
10.1109/TCI.2019.2948776
10.1109/TIP.2016.2556582
10.1109/TAES.2018.2807283
10.1109/TIP.2019.2957939
10.1017/cbo9780511976667
10.1109/LGRS.2019.2943069
10.1109/TGRS.2019.2958067
10.1103/PhysRevLett.103.150502
10.1109/MSP.2014.2312834
10.23919/IRS.2019.8768138
10.1109/TGRS.2018.2803802
10.1038/nphys3272
10.1109/TAP.2017.2734165
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID 97E
RIA
RIE
AAYXX
CITATION
7SC
7SP
7TG
7UA
8FD
C1K
F1W
FR3
H8D
H96
JQ2
KL.
KR7
L.G
L7M
L~C
L~D
DOI 10.1109/LGRS.2021.3104029
DatabaseName IEEE Xplore (IEEE)
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
Computer and Information Systems Abstracts
Electronics & Communications Abstracts
Meteorological & Geoastrophysical Abstracts
Water Resources Abstracts
Technology Research Database
Environmental Sciences and Pollution Management
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
Aerospace Database
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
ProQuest Computer Science Collection
Meteorological & Geoastrophysical Abstracts - Academic
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Technology Research Database
Computer and Information Systems Abstracts – Academic
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Water Resources Abstracts
Environmental Sciences and Pollution Management
Computer and Information Systems Abstracts Professional
Aerospace Database
Meteorological & Geoastrophysical Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
Meteorological & Geoastrophysical Abstracts - Academic
DatabaseTitleList Civil Engineering Abstracts
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore Digital Libary (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Geography
Geology
EISSN 1558-0571
EndPage 5
ExternalDocumentID 10_1109_LGRS_2021_3104029
9517316
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 61871396
  funderid: 10.13039/501100001809
– fundername: Innovative Talents Promotion Plan in Shaanxi Province
  grantid: 2020KJXX-011
– fundername: National University of Defense Technology
  grantid: 19-QNCXJ
  funderid: 10.13039/501100007085
– fundername: Key Research and Development Program of Shaanxi
  grantid: 2019ZDLGY09-01
GroupedDBID 0R~
29I
4.4
5GY
5VS
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACIWK
AENEX
AETIX
AFRAH
AGQYO
AGSQL
AHBIQ
AIBXA
AKJIK
AKQYR
ALMA_UNASSIGNED_HOLDINGS
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
EBS
EJD
HZ~
H~9
IFIPE
IPLJI
JAVBF
LAI
M43
O9-
OCL
P2P
RIA
RIE
RNS
~02
AAYXX
CITATION
7SC
7SP
7TG
7UA
8FD
C1K
F1W
FR3
H8D
H96
JQ2
KL.
KR7
L.G
L7M
L~C
L~D
ID FETCH-LOGICAL-c293t-f6267b0f4c8e8f068525b860e71ab6a31986f3577b87e01b69c0d249ba62a0c03
IEDL.DBID RIE
ISICitedReferencesCount 6
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000731151800031&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1545-598X
IngestDate Mon Jun 30 12:41:59 EDT 2025
Sat Nov 29 05:54:08 EST 2025
Tue Nov 18 22:23:52 EST 2025
Wed Aug 27 05:11:40 EDT 2025
IsPeerReviewed true
IsScholarly true
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
https://doi.org/10.15223/policy-029
https://doi.org/10.15223/policy-037
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c293t-f6267b0f4c8e8f068525b860e71ab6a31986f3577b87e01b69c0d249ba62a0c03
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-2773-3437
0000-0002-4870-1522
0000-0001-8484-8794
0000-0003-1460-4289
PQID 2610990135
PQPubID 75725
PageCount 5
ParticipantIDs ieee_primary_9517316
crossref_citationtrail_10_1109_LGRS_2021_3104029
proquest_journals_2610990135
crossref_primary_10_1109_LGRS_2021_3104029
PublicationCentury 2000
PublicationDate 20220000
2022-00-00
20220101
PublicationDateYYYYMMDD 2022-01-01
PublicationDate_xml – year: 2022
  text: 20220000
PublicationDecade 2020
PublicationPlace Piscataway
PublicationPlace_xml – name: Piscataway
PublicationTitle IEEE geoscience and remote sensing letters
PublicationTitleAbbrev LGRS
PublicationYear 2022
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref12
ref15
ref14
ref11
ref10
ref2
ref1
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref2
  doi: 10.1109/TCI.2017.2750330
– ident: ref5
  doi: 10.1109/JSTARS.2013.2263309
– ident: ref7
  doi: 10.1109/TCI.2019.2948776
– ident: ref6
  doi: 10.1109/TIP.2016.2556582
– ident: ref11
  doi: 10.1109/TAES.2018.2807283
– ident: ref9
  doi: 10.1109/TIP.2019.2957939
– ident: ref13
  doi: 10.1017/cbo9780511976667
– ident: ref12
  doi: 10.1109/LGRS.2019.2943069
– ident: ref4
  doi: 10.1109/TGRS.2019.2958067
– ident: ref14
  doi: 10.1103/PhysRevLett.103.150502
– ident: ref3
  doi: 10.1109/MSP.2014.2312834
– ident: ref10
  doi: 10.23919/IRS.2019.8768138
– ident: ref1
  doi: 10.1109/TGRS.2018.2803802
– ident: ref15
  doi: 10.1038/nphys3272
– ident: ref8
  doi: 10.1109/TAP.2017.2734165
SSID ssj0024887
Score 2.3491352
Snippet The sparse-driven radar imaging can obtain high-resolution images about a target scene with the down-sampled data. However, the huge computational complexity...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 1
SubjectTerms Algorithms
Circuit design
Complexity
Compressive sensing (CS)
Computational complexity
Computer applications
Image reconstruction
Image resolution
Imaging
Imaging techniques
Logic gates
Mathematical model
quantum algorithm
Quantum circuit
Radar
Radar data
Radar imaging
Recovery
Registers
sparse recovery
Title Sparse Reconstruction for Radar Imaging Based on Quantum Algorithms
URI https://ieeexplore.ieee.org/document/9517316
https://www.proquest.com/docview/2610990135
Volume 19
WOSCitedRecordID wos000731151800031&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: PRVIEE
  databaseName: IEEE Xplore Digital Libary (IEL)
  customDbUrl:
  eissn: 1558-0571
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0024887
  issn: 1545-598X
  databaseCode: RIE
  dateStart: 20040101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED42UfTFH5vidEoefBLr0rRN0sc5dApjqFPZW2nSVAW3yX4I_vdesro9KIJvhSalfJf07sv17gM4yahmOspCT6aaeWGW5p7089ALlQ5kFnMt3FH2U0d0u7Lfj29LcLaohTHGuJ_PzLm9dLn8bKRn9qisgdGAFVoqQ1kIPq_VWvbVk04Mz0YEXhTLfpHB9Gnc6LTve8gEmY8EFdesiyaXPsiJqvz4Ejv3crX1vxfbhs0ijCTNud13oGSGFVgvFM1fPiuw1naSvZ9VaPXekbwaYonmsl0swWCV3KdZOiY3A6dURC7QoWUEb93NEO7ZgDTfnkfj1-nLYLILj1eXD61rr5BO8DT676mXI08RiuahlkbmlMuIRUpyaoSfKp7ivpM8DyIhlBSG-orHmmbIxFTKWUo1DfZgZTgamn0g-Ci0phZItUyoGFWMGRMJFgVMofuLa0C_wUx00Vfcylu8JY5f0Dix-CcW_6TAvwaniynv86Yafw2uWsAXAwusa1D_tlhSbLtJwvg80RdEB7_POoQNZusX3BlKHVYQdHMEq_pj-joZH7sV9QWte8Y5
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwEB1BAcGFHVFWHzghAo4Tx84RKqCIUrGrtyh2HEDqpi5I_XvGbmgPICRukWJH0Rs7M8-TmQdwlFHNNM9CT6aaeWGW5p7089ALlQ5kFkdauKPs15qo12WjEd_PwMmkFsYY434-M6f20uXys44e2qOyM4wGrNDSLMzxMGR0XK017awnnRyejQk8HstGkcP0aXxWu358Qi7IfKSouGpdPDn1Qk5W5ce32DmYq5X_vdoqLBeBJDkfW34NZkx7HRYLTfP30TosXDvR3tEGVJ66SF8NsVRz2jCWYLhKHtMs7ZGbltMqIhfo0jKCtx6GCPiwRc6bb53ex-C91d-El6vL50rVK8QTPI0efODlyFSEonmopZE5jSRnXMmIGuGnKkpx58koD7gQSgpDfRXFmmbIxVQasZRqGmxBqd1pm20g-Ci0pxZItkyoGFWMGcMF4wFT6ADjMtBvMBNddBa3AhfNxDEMGicW_8TinxT4l-F4MqU7bqvx1-ANC_hkYIF1Gfa-LZYUG6-fsGic6gv4zu-zDmGx-nxXS2o39dtdWGK2msGdqOxBCQ1g9mFefw4--r0Dt7q-ACvEyYA
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=Sparse+Reconstruction+for+Radar+Imaging+Based+on+Quantum+Algorithms&rft.jtitle=IEEE+geoscience+and+remote+sensing+letters&rft.au=Liu%2C+Xiaowen&rft.au=Dong%2C+Chen&rft.au=Luo%2C+Ying&rft.au=Kang%2C+Le&rft.date=2022&rft.issn=1545-598X&rft.eissn=1558-0571&rft.volume=19&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FLGRS.2021.3104029&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_LGRS_2021_3104029
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1545-598X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1545-598X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1545-598X&client=summon