Dynamic and Robust Sensor Selection Strategies for Wireless Positioning With TOA/RSS Measurement

Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scena...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 72; no. 11; pp. 1 - 16
Main Authors Oh, Myeung Suk, Hosseinalipour, Seyyedali, Kim, Taejoon, Love, David J., Krogmeier, James V., Brinton, Christopher G.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cramér-Rao lower bound (CRLB) as a performance metric to quantify the positioning accuracy resulted from selected sensors. For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions. These selection strategies are shown to strike an efficient balance between computational complexity and performance suboptimality. For robust sensor selection, we show that the conventional convex relaxation approach leads to instability, and then develop three algorithms based on (i) iterative convex optimization (ICO), (ii) difference of convex functions programming (DCP), and (iii) discrete monotonic optimization (DMO). Each of these strategies exhibits a different tradeoff between computational complexity and optimality guarantee. Simulation results show that the proposed sensor selection strategies provide significant improvements in terms of accuracy and/or complexity compared to existing sensor selection methods.
AbstractList Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cramér-Rao lower bound (CRLB) as a performance metric to quantify the positioning accuracy resulted from selected sensors. For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions. These selection strategies are shown to strike an efficient balance between computational complexity and performance suboptimality. For robust sensor selection, we show that the conventional convex relaxation approach leads to instability, and then develop three algorithms based on (i) iterative convex optimization (ICO), (ii) difference of convex functions programming (DCP), and (iii) discrete monotonic optimization (DMO). Each of these strategies exhibits a different tradeoff between computational complexity and optimality guarantee. Simulation results show that the proposed sensor selection strategies provide significant improvements in terms of accuracy and/or complexity compared to existing sensor selection methods.
Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this article, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cramér-Rao lower bound (CRLB) as a performance metric to quantify the positioning accuracy resulted from selected sensors. For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions. These selection strategies are shown to strike an efficient balance between computational complexity and performance suboptimality. For robust sensor selection, we show that the conventional convex relaxation approach leads to instability, and then develop three algorithms based on (i) iterative convex optimization (ICO), (ii) difference of convex functions programming (DCP), and (iii) discrete monotonic optimization (DMO). Each of these strategies exhibits a different tradeoff between computational complexity and optimality guarantee. Simulation results show that the proposed sensor selection strategies provide significant improvements in terms of accuracy and/or complexity compared to existing sensor selection methods.
Author Kim, Taejoon
Oh, Myeung Suk
Krogmeier, James V.
Love, David J.
Hosseinalipour, Seyyedali
Brinton, Christopher G.
Author_xml – sequence: 1
  givenname: Myeung Suk
  orcidid: 0000-0002-1996-3128
  surname: Oh
  fullname: Oh, Myeung Suk
  organization: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
– sequence: 2
  givenname: Seyyedali
  orcidid: 0000-0003-4266-4000
  surname: Hosseinalipour
  fullname: Hosseinalipour, Seyyedali
  organization: Department of Electrical Engineering, University at Buffalo-SUNY, Amherst, NY, USA
– sequence: 3
  givenname: Taejoon
  orcidid: 0000-0002-4017-9530
  surname: Kim
  fullname: Kim, Taejoon
  organization: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA
– sequence: 4
  givenname: David J.
  orcidid: 0000-0001-5922-4787
  surname: Love
  fullname: Love, David J.
  organization: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
– sequence: 5
  givenname: James V.
  orcidid: 0000-0001-7041-2113
  surname: Krogmeier
  fullname: Krogmeier, James V.
  organization: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
– sequence: 6
  givenname: Christopher G.
  orcidid: 0000-0003-2771-3521
  surname: Brinton
  fullname: Brinton, Christopher G.
  organization: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
BookMark eNpNkL1vwjAQxa2KSgXavUOHSJ0D_ohje0T0U6KiImk7psacaRA41E4G_vsawdDp6e69dyf9BqjnGgcI3RI8IgSrcflZjiimbMSoUJKxC9QniqlUMa56qI8xkaniGb9CgxA2ccwyRfro--Hg9K42iXarZNEsu9AmBbjQ-ChbMG3duKRovW5hXUNIbDS-ah-tEJL3JtTHQO3Wcdn-JOV8Ml4URfIGOnQeduDaa3Rp9TbAzVmH6OPpsZy-pLP58-t0MksNzXibciG4NBnVllultc1BYLOyS841E4QKi8FQTuRKMZ1JgiXFGc1zboQVRGBgQ3R_urv3zW8Hoa02TeddfFlRqTDBXOQipvApZXwTggdb7X290_5QEVwdOVaRY3XkWJ05xsrdqVIDwL84YZJFwn8QBHAZ
CODEN ITVTAB
Cites_doi 10.1109/TAES.1976.308294
10.1109/LCOMM.2004.835319
10.1109/milcom.2008.4753258
10.1214/aoms/1177729893
10.1109/TWC.2014.2356507
10.1109/TWC.2012.040412.110697
10.1109/JPROC.2003.814918
10.1007/978-1-4419-8853-9
10.1109/LCOMM.2018.2833544
10.1017/CBO9780511804441
10.1109/TSP.2008.2007095
10.1109/GLOBECOM42002.2020.9322149
10.1007/s11277-017-4734-x
10.1109/MSP.2005.1458275
10.3390/s16050707
10.1137/04060932X
10.1109/78.258082
10.1109/icassp.2002.5745148
10.1109/MWC.2011.5751291
10.1109/MSP.2005.1458284
10.1109/MWC.001.2000259
10.1109/TSP.2011.2170170
10.1109/JPROC.2008.2008840
10.1002/asl.128
10.1109/MSP.2005.1458289
10.1109/ChiCC.2014.6896643
10.1007/s11081-015-9294-x
10.1117/12.723514
10.1109/TEVC.2021.3085906
10.1109/TVT.2020.3011118
10.1109/IPIN.2019.8911771
10.1109/ieeestd.2020.9179124
10.1109/TVT.2019.2936110
10.1109/TIM.2022.3191705
10.1109/TSP.2011.2160630
10.1109/TSP.2003.814469
10.1002/ett.1530
10.1109/TIM.2012.2209918
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID 97E
RIA
RIE
AAYXX
CITATION
7SP
8FD
FR3
KR7
L7M
DOI 10.1109/TVT.2023.3279833
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005-present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library Online
CrossRef
Electronics & Communications Abstracts
Technology Research Database
Engineering Research Database
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
DatabaseTitle CrossRef
Civil Engineering Abstracts
Engineering Research Database
Technology Research Database
Advanced Technologies Database with Aerospace
Electronics & Communications Abstracts
DatabaseTitleList
Civil Engineering Abstracts
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1939-9359
EndPage 16
ExternalDocumentID 10_1109_TVT_2023_3279833
10138319
Genre orig-research
GrantInformation_xml – fundername: National Science Foundation (NSF)
  grantid: CNS2146171; CNS2212565; CNS2225577
GroupedDBID -~X
.DC
0R~
29I
4.4
5GY
6IK
97E
AAIKC
AAJGR
AAMNW
AASAJ
ABQJQ
ABVLG
ACGFO
ACGFS
ACIWK
ACNCT
AENEX
AKJIK
ALMA_UNASSIGNED_HOLDINGS
ASUFR
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
HZ~
IFIPE
IPLJI
JAVBF
LAI
M43
MS~
O9-
OCL
P2P
RIA
RIE
RIG
RNS
RXW
TAE
TN5
3EH
5VS
AAYOK
AAYXX
AETIX
AI.
AIBXA
ALLEH
CITATION
EJD
H~9
IAAWW
IBMZZ
ICLAB
IFJZH
VH1
7SP
8FD
FR3
KR7
L7M
ID FETCH-LOGICAL-c245t-57758c42af5f9aaf6e70cdfb55a37127f0ec2518d93a481082042665c7f7170e3
IEDL.DBID RIE
ISSN 0018-9545
IngestDate Thu Oct 10 17:36:24 EDT 2024
Fri Aug 23 01:01:08 EDT 2024
Wed Jun 26 19:25:39 EDT 2024
IsPeerReviewed true
IsScholarly true
Issue 11
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c245t-57758c42af5f9aaf6e70cdfb55a37127f0ec2518d93a481082042665c7f7170e3
ORCID 0000-0003-4266-4000
0000-0002-4017-9530
0000-0003-2771-3521
0000-0002-1996-3128
0000-0001-5922-4787
0000-0001-7041-2113
PQID 2890105767
PQPubID 85454
PageCount 16
ParticipantIDs ieee_primary_10138319
crossref_primary_10_1109_TVT_2023_3279833
proquest_journals_2890105767
PublicationCentury 2000
PublicationDate 2023-11-01
PublicationDateYYYYMMDD 2023-11-01
PublicationDate_xml – month: 11
  year: 2023
  text: 2023-11-01
  day: 01
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
PublicationTitle IEEE transactions on vehicular technology
PublicationTitleAbbrev TVT
PublicationYear 2023
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 ref35
ref12
ref34
ref15
ref37
ref14
ref36
ref11
ref33
ref10
ref32
ref2
ref1
ref17
ref39
ref16
ref38
ref19
ref18
Kay (ref13) 1997
ref24
ref23
ref26
ref25
ref20
Krause (ref30) 2008; 9
ref41
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
Powers (ref31) 2016
ref40
References_xml – ident: ref29
  doi: 10.1109/TAES.1976.308294
– ident: ref15
  doi: 10.1109/LCOMM.2004.835319
– ident: ref25
  doi: 10.1109/milcom.2008.4753258
– volume: 9
  start-page: 2761
  issue: 93
  year: 2008
  ident: ref30
  article-title: Robust submodular observation selection
  publication-title: J. Mach. Learn. Res.
  contributor:
    fullname: Krause
– ident: ref34
  doi: 10.1214/aoms/1177729893
– ident: ref40
  doi: 10.1109/TWC.2014.2356507
– ident: ref26
  doi: 10.1109/TWC.2012.040412.110697
– volume-title: Fundamentals of Statistical Signal Processing: Estimation Theory
  year: 1997
  ident: ref13
  contributor:
    fullname: Kay
– ident: ref1
  doi: 10.1109/JPROC.2003.814918
– ident: ref36
  doi: 10.1007/978-1-4419-8853-9
– ident: ref17
  doi: 10.1109/LCOMM.2018.2833544
– ident: ref33
  doi: 10.1017/CBO9780511804441
– ident: ref35
  doi: 10.1109/TSP.2008.2007095
– ident: ref41
  doi: 10.1109/GLOBECOM42002.2020.9322149
– ident: ref9
  doi: 10.1007/s11277-017-4734-x
– ident: ref3
  doi: 10.1109/MSP.2005.1458275
– ident: ref8
  doi: 10.3390/s16050707
– ident: ref39
  doi: 10.1137/04060932X
– ident: ref37
  doi: 10.1109/78.258082
– ident: ref14
  doi: 10.1109/icassp.2002.5745148
– ident: ref2
  doi: 10.1109/MWC.2011.5751291
– ident: ref6
  doi: 10.1109/MSP.2005.1458284
– ident: ref4
  doi: 10.1109/MWC.001.2000259
– ident: ref21
  doi: 10.1109/TSP.2011.2170170
– ident: ref5
  doi: 10.1109/JPROC.2008.2008840
– ident: ref28
  doi: 10.1002/asl.128
– ident: ref7
  doi: 10.1109/MSP.2005.1458289
– ident: ref27
  doi: 10.1109/ChiCC.2014.6896643
– ident: ref38
  doi: 10.1007/s11081-015-9294-x
– start-page: 2179
  volume-title: Proc. IEEE 19th Int. Conf. Inf. Fusion
  year: 2016
  ident: ref31
  article-title: Constrained robust submodular sensor selection with applications to multistatic sonar arrays
  contributor:
    fullname: Powers
– ident: ref12
  doi: 10.1117/12.723514
– ident: ref20
  doi: 10.1109/TEVC.2021.3085906
– ident: ref23
  doi: 10.1109/TVT.2020.3011118
– ident: ref18
  doi: 10.1109/IPIN.2019.8911771
– ident: ref11
  doi: 10.1109/ieeestd.2020.9179124
– ident: ref22
  doi: 10.1109/TVT.2019.2936110
– ident: ref19
  doi: 10.1109/TIM.2022.3191705
– ident: ref32
  doi: 10.1109/TSP.2011.2160630
– ident: ref10
  doi: 10.1109/TSP.2003.814469
– ident: ref16
  doi: 10.1002/ett.1530
– ident: ref24
  doi: 10.1109/TIM.2012.2209918
SSID ssj0014491
Score 2.4710808
Snippet Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection...
Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this article, we develop sensor selection...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Publisher
StartPage 1
SubjectTerms Accuracy
Algorithms
Approximation
Complexity
Convex analysis
Convexity
Cramér-Rao lower bound
Heuristic algorithms
Iterative methods
Lower bounds
Optimization
Position measurement
received signal strength
sensor selection
Sensors
Signal strength
Three-dimensional displays
time of arrival
Vehicle dynamics
Wireless communication
Wireless positioning
Wireless sensor networks
Title Dynamic and Robust Sensor Selection Strategies for Wireless Positioning With TOA/RSS Measurement
URI https://ieeexplore.ieee.org/document/10138319
https://www.proquest.com/docview/2890105767
Volume 72
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3NT4MwFG90Jz34OeN0mh68eIABpVCOi7osJptmH2Y3LG2JiQuYDS7-9b4W0Kkx8QQhpWn62r7f63vv9xC6EgAZmJLKipQAA0VIabGAEAu0e0KdQIGW1YnCo3EwnPv3C7qok9VNLoxSygSfKVu_Gl--zEWpr8pgh7tgUGmSz23meFWy1qfLwPfr8ngu7GDABY1P0ol6s6eZrcuE28QLI0bINx1kiqr8OomNehnso3EzsCqq5NUui8QW7z84G_898gO0VwNN3K9WxiHaUtkR2t2gHzxGz7dVOXrMM4kneVKuCzwFszZfwWNpYrQy3NDXqjUGfIt1tOwSTkf8WEd7QU_wsXjBs4d-bzKd4tHXrWMbzQd3s5uhVVdcsITn08KiIZgPwvd4StOI8zRQoSNkmlDKSeh6YeooAYCIyYhwn7kaPmgNT0WYglnoKHKCWlmeqVOEGacBkYKJQBqvM08T35OcuZorMo28DrpuZBC_VcQasTFInCgGecVaXnEtrw5q6yndaFfNZgd1G6nF9dZbx9pzqosXB-HZH7-dox3de5VR2EWtYlWqC4AWRXJpltQH2f7J0Q
link.rule.ids 315,786,790,802,27955,27956,55107
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV07T8MwELZQGYCBZxGFAh5YGJImcZzHWAFVgbagNkXdgmM7QqJKUZsu_HrOTgIFhMSUKHISy2f7vvPdfYfQBQfIEEghjVByMFC4EEbgEWKAdk-o5UnQsipRuD_wumP3bkInZbK6zoWRUurgM2mqW-3LFzO-VEdlsMJtMKgUyec6KHrLL9K1Pp0GrlsWyLNhDQMyqLySVtiKniJTFQo3ieOHASHftJAuq_JrL9YKprODBlXXiriSV3OZJyZ__8Ha-O--76LtEmridjE39tCazPbR1goB4QF6vi4K0mOWCTycJctFjkdg2M7mcJnqKK0MVwS2coEB4WIVLzuF_RE_lvFe8CV4mL_g6KHdGo5GuP917lhH485NdNU1ypoLBndcmhvUBwOCuw5LaRoylnrSt7hIE0oZ8W3HTy3JARIFIiTMDWwFIJSOp9xPwTC0JDlEtWyWySOEA0Y9InjAPaH9zixNXEewwFZskWnoNNBlJYP4raDWiLVJYoUxyCtW8opLeTVQXQ3pSrtiNBuoWUktLhffIla-U1W-2POP_3jtHG10o34v7t0O7k_QpvpTkV_YRLV8vpSnADTy5ExPrw-pfs0l
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=Dynamic+and+Robust+Sensor+Selection+Strategies+for+Wireless+Positioning+With+TOA%2FRSS+Measurement&rft.jtitle=IEEE+transactions+on+vehicular+technology&rft.au=Oh%2C+Myeung+Suk&rft.au=Hosseinalipour%2C+Seyyedali&rft.au=Kim%2C+Taejoon&rft.au=Love%2C+David+J.&rft.date=2023-11-01&rft.pub=IEEE&rft.issn=0018-9545&rft.eissn=1939-9359&rft.spage=1&rft.epage=16&rft_id=info:doi/10.1109%2FTVT.2023.3279833&rft.externalDocID=10138319
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9545&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9545&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9545&client=summon