View in EDS

Sparsity-promoting sensor selection for nonlinear measurement models

Saved in:
Bibliographic Details
Title: Sparsity-promoting sensor selection for nonlinear measurement models
Authors: Sundeep Prabhakar Chepuri, Student Member, Geert Leus
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://ens.ewi.tudelft.nl/pubs/sundeep15tsp.pdf.
Publication Year: 2013
Collection: CiteSeerX
Subject Terms: sensor placement
Description: —The problem of choosing the best subset of sensors that guarantees a certain estimation performance is referred to as sensor selection. In this paper, we focus on observations that are related to a general non-linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cramér–Rao bound (CRB) as a performance measure. We formu-late the sensor selection problem as the design of a sparse vector, which in its original form is a nonconvex-(quasi) norm optimiza-tion problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. The proposed solvers re-sult in sparse sensing techniques. We also propose a projected sub-gradient algorithm that is attractive for large-scale problems. The developed theory is applied to sensor placement for localization. Index Terms—Convex optimization, Cramér–Rao bound, non-linear models, projected subgradient algorithm, sensor networks
Document Type: text
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.3644; http://ens.ewi.tudelft.nl/pubs/sundeep15tsp.pdf
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.3644
http://ens.ewi.tudelft.nl/pubs/sundeep15tsp.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.5FD395EE
Database: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.3644#
    Name: EDS - BASE (s4221598)
    Category: fullText
    Text: View record from BASE
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Chepuri%20SP
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsbas
DbLabel: BASE
An: edsbas.5FD395EE
RelevancyScore: 845
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 845.314270019531
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Sparsity-promoting sensor selection for nonlinear measurement models
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Sundeep+Prabhakar+Chepuri%22">Sundeep Prabhakar Chepuri</searchLink><br /><searchLink fieldCode="AR" term="%22Student+Member%22">Student Member</searchLink><br /><searchLink fieldCode="AR" term="%22Geert+Leus%22">Geert Leus</searchLink>
– Name: Author
  Label: Contributors
  Group: Au
  Data: The Pennsylvania State University CiteSeerX Archives
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>http://ens.ewi.tudelft.nl/pubs/sundeep15tsp.pdf</i>.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2013
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: CiteSeerX
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22sensor+placement%22">sensor placement</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: —The problem of choosing the best subset of sensors that guarantees a certain estimation performance is referred to as sensor selection. In this paper, we focus on observations that are related to a general non-linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cramér–Rao bound (CRB) as a performance measure. We formu-late the sensor selection problem as the design of a sparse vector, which in its original form is a nonconvex-(quasi) norm optimiza-tion problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. The proposed solvers re-sult in sparse sensing techniques. We also propose a projected sub-gradient algorithm that is attractive for large-scale problems. The developed theory is applied to sensor placement for localization. Index Terms—Convex optimization, Cramér–Rao bound, non-linear models, projected subgradient algorithm, sensor networks
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: text
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: application/pdf
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.3644; http://ens.ewi.tudelft.nl/pubs/sundeep15tsp.pdf
– Name: URL
  Label: Availability
  Group: URL
  Data: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.3644<br />http://ens.ewi.tudelft.nl/pubs/sundeep15tsp.pdf
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.5FD395EE
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.5FD395EE
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: sensor placement
        Type: general
    Titles:
      – TitleFull: Sparsity-promoting sensor selection for nonlinear measurement models
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sundeep Prabhakar Chepuri
      – PersonEntity:
          Name:
            NameFull: Student Member
      – PersonEntity:
          Name:
            NameFull: Geert Leus
      – PersonEntity:
          Name:
            NameFull: The Pennsylvania State University CiteSeerX Archives
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2013
          Identifiers:
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
          Titles:
            – TitleFull: http://ens.ewi.tudelft.nl/pubs/sundeep15tsp.pdf
              Type: main
ResultId 1