Further results on cooperative localization via semidefinite programming

Ning Wang, Liuqing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

As a powerful tool to convert nonconvex problems into convex ones, semidefinite programing (SDP) has been introduced to both cooperative and non-cooperative localization systems. In this paper, we derive the Cramér-Rao Lower Bound (CRLB) for several scenarios to show the advantage of cooperative localization. We then consider cooperative localization via SDP using various semidefinite relaxations, including existing Standard SDP (SSDP), Edge-based SDP (ESDP), Node-based SDP (NSDP) and our proposed Component-wise SDP (CSDP). We analyze their performances and complexity and find that CSDP has advantages in both aspects. Simulations will also be carried out to corroborate our analyses.

Original languageEnglish (US)
Title of host publication2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 45th Annual Conference on Information Sciences and Systems, CISS 2011 - Baltimore, MD, United States
Duration: Mar 23 2011Mar 25 2011

Publication series

Name2011 45th Annual Conference on Information Sciences and Systems, CISS 2011

Other

Other2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
CountryUnited States
CityBaltimore, MD
Period3/23/113/25/11

Keywords

  • Cooperative Localization
  • Semidefinite Programming (SDP)

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