Semidefinite Programming for Wireless Cooperative Localization Using Biased RSS Measurements

Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are gene...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE communications letters Jg. 26; H. 6; S. 1278 - 1282
Hauptverfasser: Wang, Qi, Duan, Zhansheng, Li, Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bias
Convexity
cooperative localization
Localization
Location awareness
Maximum likelihood estimation
Maximum likelihood estimators
Measurement uncertainty
Nodes
Noise measurement
Norms
observation bias
Optimization
RSS
SDP
Semidefinite programming
Signal strength
Time measurement
Wireless communication
Wireless sensor networks
ISSN:1089-7798, 1558-2558
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are generally assumed bias-free. However, in practice, they may be subject to biases, which directly affect localization accuracy. As a result, the existing localization methods are not applicable any more. In this letter, RSS observation biases are considered as the extra parameters to be estimated as well as locations of target nodes. To overcome the nonconvexity of the maximum likelihood (ML) estimator, semidefinite programming (SDP) is applied with <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula> norms, respectively. Then, the locations of multiple target nodes and observation biases are simultaneously estimated through convex optimization. Numerical examples demonstrate the performance superiority of the proposed methods compared to the existing bias-free SDP methods for wireless cooperative localization.
AbstractList Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are generally assumed bias-free. However, in practice, they may be subject to biases, which directly affect localization accuracy. As a result, the existing localization methods are not applicable any more. In this letter, RSS observation biases are considered as the extra parameters to be estimated as well as locations of target nodes. To overcome the nonconvexity of the maximum likelihood (ML) estimator, semidefinite programming (SDP) is applied with <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula> norms, respectively. Then, the locations of multiple target nodes and observation biases are simultaneously estimated through convex optimization. Numerical examples demonstrate the performance superiority of the proposed methods compared to the existing bias-free SDP methods for wireless cooperative localization.
Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are generally assumed bias-free. However, in practice, they may be subject to biases, which directly affect localization accuracy. As a result, the existing localization methods are not applicable any more. In this letter, RSS observation biases are considered as the extra parameters to be estimated as well as locations of target nodes. To overcome the nonconvexity of the maximum likelihood (ML) estimator, semidefinite programming (SDP) is applied with [Formula Omitted] and [Formula Omitted] norms, respectively. Then, the locations of multiple target nodes and observation biases are simultaneously estimated through convex optimization. Numerical examples demonstrate the performance superiority of the proposed methods compared to the existing bias-free SDP methods for wireless cooperative localization.
Author Duan, Zhansheng
Wang, Qi
Li, Fei
Author_xml – sequence: 1
  givenname: Qi
  orcidid: 0000-0002-0122-2591
  surname: Wang
  fullname: Wang, Qi
  email: wangqi@xsyu.edu.cn
  organization: School of Electronic Engineering, Xi'an Shiyou University, Xi'an, China
– sequence: 2
  givenname: Zhansheng
  orcidid: 0000-0001-7366-5984
  surname: Duan
  fullname: Duan, Zhansheng
  email: zsduan@mail.xjtu.edu.cn
  organization: Center for Information Engineering Science Research, Xi'an Jiaotong University, Xi'an, China
– sequence: 3
  givenname: Fei
  surname: Li
  fullname: Li, Fei
  email: lif@xsyu.edu.cn
  organization: School of Electronic Engineering, Xi'an Shiyou University, Xi'an, China
BookMark eNp9kE1Lw0AQhhdRsK3-Ab0EPKfObrLZzVGDX5BSsRYvQtgkk7KlydbdVNBf7_YDDx68zBfvM8O8Q3LcmQ4JuaAwphTS6zybTiZjBoyNI5okQsIRGVDOZch8OPY1yDQUIpWnZOjcEgAk43RA3mfY6hob3ekeg2drFla1re4WQWNs8KYtrtC5IDNmjVb1-hOD3FRqpb99Y7pg7rbaW60c1sHLbBZMULmNxRa73p2Rk0atHJ4f8ojM7-9es8cwnz48ZTd5WDHG-7BKYlZClaQgy0qWEQAKFYOsOW9iUFEJQP285mmtoMYE44qzRqqEKsE8Fo3I1X7v2pqPDbq-WJqN7fzJgiWCQyzTSHgV26sqa5yz2BRrq1tlvwoKxdbFYudisXWxOLjoIfkHqnS_e723Sq_-Ry_3qEbE31up4AmwNPoBneSC1Q
CODEN ICLEF6
CitedBy_id crossref_primary_10_1016_j_future_2023_07_016
crossref_primary_10_1016_j_phycom_2023_102165
crossref_primary_10_1109_JSAC_2024_3413973
crossref_primary_10_1109_JIOT_2024_3407535
crossref_primary_10_1109_TWC_2024_3441643
crossref_primary_10_1109_JIOT_2024_3403972
crossref_primary_10_1109_JSAC_2023_3240714
crossref_primary_10_1109_LCOMM_2025_3550860
crossref_primary_10_1109_TITS_2023_3287209
crossref_primary_10_1109_JIOT_2023_3244982
crossref_primary_10_1177_01423312221148787
crossref_primary_10_1109_TVT_2023_3295868
crossref_primary_10_1155_2023_8931636
Cites_doi 10.1109/CSO.2009.447
10.1109/TSP.2012.2232664
10.1080/10556789908805766
10.1109/TSP.2010.2045416
10.1109/LCOMM.2018.2849963
10.1109/LSENS.2021.3125911
10.1109/TWC.2013.120613.130170
10.1109/TSP.2003.814469
10.1017/CBO9780511804441
10.1109/LSENS.2017.2787651
10.1109/TAES.2020.2999999
10.23919/ICIF.2018.8455659
10.1109/LSP.2020.3005298
10.1109/7.826314
10.1109/TVT.2010.2040096
10.1109/TSP.2015.2394300
10.1109/LCOMM.2014.2318031
10.1137/1038003
10.1109/LSP.2008.916731
10.1109/TVT.2014.2334397
10.1109/TAES.2019.2929998
10.1137/1.9781611970791
10.1109/LSP.2019.2892225
10.1109/TVT.2021.3089161
10.1109/TVT.2018.2880991
10.1109/TSP.2011.2152400
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
7SP
8FD
L7M
DOI 10.1109/LCOMM.2022.3166780
DatabaseName IEEE Xplore (IEEE)
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Xplore
CrossRef
Electronics & Communications Abstracts
Technology Research Database
Advanced Technologies Database with Aerospace
DatabaseTitle CrossRef
Technology Research Database
Advanced Technologies Database with Aerospace
Electronics & Communications Abstracts
DatabaseTitleList
Technology Research Database
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1558-2558
EndPage 1282
ExternalDocumentID 10_1109_LCOMM_2022_3166780
9756029
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: U20B2029
  funderid: 10.13039/501100001809
– fundername: Natural Science Basic Research Plan in Shaanxi Province of China
  grantid: 2022JQ-641
  funderid: 10.13039/501100007128
– fundername: National Key Research and Development Plan
  grantid: 2021YFC2202600; 2021YFC2202603
GroupedDBID -~X
0R~
29I
4.4
5GY
5VS
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACIWK
AENEX
AETIX
AGQYO
AGSQL
AHBIQ
AI.
AIBXA
AKJIK
AKQYR
ALLEH
ALMA_UNASSIGNED_HOLDINGS
ATWAV
AZLTO
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
HZ~
H~9
IES
IFIPE
IFJZH
IPLJI
JAVBF
LAI
M43
O9-
OCL
P2P
RIA
RIE
RNS
TN5
VH1
AAYXX
CITATION
7SP
8FD
L7M
ID FETCH-LOGICAL-c225t-c642b0c6908bc8b300e7a408d55f40a3b001c8bd59da0de6e4c52f8a61a72c693
IEDL.DBID RIE
ISICitedReferencesCount 14
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000809396900019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1089-7798
IngestDate Mon Jun 30 10:23:15 EDT 2025
Tue Nov 18 20:42:38 EST 2025
Sat Nov 29 03:56:09 EST 2025
Wed Aug 27 02:24:34 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 6
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-c225t-c642b0c6908bc8b300e7a408d55f40a3b001c8bd59da0de6e4c52f8a61a72c693
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-0122-2591
0000-0001-7366-5984
PQID 2675048937
PQPubID 85419
PageCount 5
ParticipantIDs proquest_journals_2675048937
crossref_primary_10_1109_LCOMM_2022_3166780
crossref_citationtrail_10_1109_LCOMM_2022_3166780
ieee_primary_9756029
PublicationCentury 2000
PublicationDate 2022-06-01
PublicationDateYYYYMMDD 2022-06-01
PublicationDate_xml – month: 06
  year: 2022
  text: 2022-06-01
  day: 01
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
PublicationTitle IEEE communications letters
PublicationTitleAbbrev LCOMM
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
ref17
ref16
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref8
ref7
ref9
ref4
ref3
Rappaport (ref27) 1999
ref6
ref5
References_xml – ident: ref19
  doi: 10.1109/CSO.2009.447
– ident: ref14
  doi: 10.1109/TSP.2012.2232664
– ident: ref26
  doi: 10.1080/10556789908805766
– ident: ref13
  doi: 10.1109/TSP.2010.2045416
– ident: ref7
  doi: 10.1109/LCOMM.2018.2849963
– ident: ref18
  doi: 10.1109/LSENS.2021.3125911
– ident: ref11
  doi: 10.1109/TWC.2013.120613.130170
– ident: ref22
  doi: 10.1109/TSP.2003.814469
– ident: ref24
  doi: 10.1017/CBO9780511804441
– ident: ref17
  doi: 10.1109/LSENS.2017.2787651
– ident: ref21
  doi: 10.1109/TAES.2020.2999999
– ident: ref12
  doi: 10.23919/ICIF.2018.8455659
– ident: ref5
  doi: 10.1109/LSP.2020.3005298
– ident: ref20
  doi: 10.1109/7.826314
– ident: ref2
  doi: 10.1109/TVT.2010.2040096
– ident: ref4
  doi: 10.1109/TSP.2015.2394300
– ident: ref10
  doi: 10.1109/LCOMM.2014.2318031
– ident: ref23
  doi: 10.1137/1038003
– ident: ref16
  doi: 10.1109/LSP.2008.916731
– ident: ref1
  doi: 10.1109/TVT.2014.2334397
– ident: ref8
  doi: 10.1109/TAES.2019.2929998
– ident: ref25
  doi: 10.1137/1.9781611970791
– ident: ref3
  doi: 10.1109/LSP.2019.2892225
– ident: ref15
  doi: 10.1109/TVT.2021.3089161
– ident: ref6
  doi: 10.1109/TVT.2018.2880991
– ident: ref9
  doi: 10.1109/TSP.2011.2152400
– volume-title: Wireless Communications Principles and Practice
  year: 1999
  ident: ref27
SSID ssj0008251
Score 2.4420447
Snippet Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 1278
SubjectTerms Bias
Convexity
cooperative localization
Localization
Location awareness
Maximum likelihood estimation
Maximum likelihood estimators
Measurement uncertainty
Nodes
Noise measurement
Norms
observation bias
Optimization
RSS
SDP
Semidefinite programming
Signal strength
Time measurement
Wireless communication
Wireless sensor networks
Title Semidefinite Programming for Wireless Cooperative Localization Using Biased RSS Measurements
URI https://ieeexplore.ieee.org/document/9756029
https://www.proquest.com/docview/2675048937
Volume 26
WOSCitedRecordID wos000809396900019&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
  customDbUrl:
  eissn: 1558-2558
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0008251
  issn: 1089-7798
  databaseCode: RIE
  dateStart: 19970101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LS8MwGP-Yw4MefE1xOiUHb1qXpWmbHHUoHjYVp7KDUPIqDOYqe_j3m6RdVRTBW2nzlZJfv2e-B8CJijqSaCqDLOaRC91YlpIyDDJNrfKimRJ-FsFzL7m9ZcMhv6_BWVULY4zxyWfm3F36s3ydq4ULlbV5YvUz4SuwkiRxUatVSV1Xglkk03NrMXK2LJDBvN3r3vX71hUkxHqosZXO-JsS8lNVfohir1-uN__3ZVuwUdqR6KIAfhtqZrID61-6CzbgZWBeR9pkI2dWovsiEevVPkLWUEUu7XVsxRzq5vmbKfp_o57TbGVlJvLZBOhyZPWcRg-DAep_xhNnu_B0ffXYvQnKYQqBsiw7D5R1NCRW1hlmUjEZYmwSQTHTUZRRLEJnPdn7OuJaYG1iQ1VEMibijkiIJQv3oD7JJ2YfkAhDjWOiJZOUCiJZQgVTEY11B4daySZ0lrubqrLTuBt4MU69x4F56hFJHSJpiUgTTiuat6LPxp-rGw6DamW5_U1oLUFMS1acpSR2HeydWXbwO9UhrLl3F_lfLajPpwtzBKvqfT6aTY_9X_YBAWnP1w
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ZT9tAEB4BRQIeenCIUFr2oW9gslmv7d1HiIqocFJEAPGAZO1lKVIOlIPfz-zaCa1Alfpm2Tu2teOZ-WY8B8APk7Q0s1xHZSoTH7pBkdI6jkrL0Xjx0qgwi-A-z7pd8fAgr1fgZFkL45wLyWfu1B-Gf_l2bOY-VNaUGdpnJlfhA96G0apaa6l3fRFmlU4vETNKsSiRobKZt393OugMMoY-aor6mf5lhsJclTfKOFiYi0__926f4WONJMlZxfovsOJG27D1R3_BHXjsuWHfurLvgSW5rlKxhniJIFQlPvF1gIqOtMfjJ1d1ACe5t211bSYJ-QTkvI-WzpKbXo90XiOK0124u_h5276M6nEKkUGhnUUGXQ1NDbrDQhuhY0pdpjgVNklKTlXs8ROet4m0ilqXOm4SVgqVtlTGkCzeg7XReOT2gag4tjRlVgvNuWJaZFwJk_DUtmhsjW5Aa7G7hal7jfuRF4Mi-BxUFoEjhedIUXOkAcdLmqeq08Y_V-94HixX1tvfgMMFE4taGKcFS30Pew_MDt6nOoKNy9tOXuS_uldfYdM_p8oGO4S12WTuvsG6eZ71p5Pv4Yt7Afl90x4
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=Semidefinite+Programming+for+Wireless+Cooperative+Localization+Using+Biased+RSS+Measurements&rft.jtitle=IEEE+communications+letters&rft.au=Wang%2C+Qi&rft.au=Duan%2C+Zhansheng&rft.au=Li%2C+Fei&rft.date=2022-06-01&rft.pub=IEEE&rft.issn=1089-7798&rft.volume=26&rft.issue=6&rft.spage=1278&rft.epage=1282&rft_id=info:doi/10.1109%2FLCOMM.2022.3166780&rft.externalDocID=9756029
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1089-7798&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1089-7798&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1089-7798&client=summon