Source Localization with Spatially Distributed Active and Passive Sensors

Yifan Liang, Hongbin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We consider solving the source localization problem by exploiting measurements collected from both active and passive sensors. We first briefly review some existing least squares approaches that utilize only one type of measurement (active or passive), and further establish a hybrid objective function that includes active and passive measurements simultaneously in the sense of squared least squares. We propose two different methods, Newton's method and the semidefinite relaxation method, to efficiently solve the optimization problem. Simulation results indicate that the source location estimates given by the proposed hybrid methods are superior to the peer methods that only utilize one type of measurement. The performance difference between Newton's method and the semidefinite relaxation method is also investigated.

Original languageEnglish
Title of host publication2024 58th Annual Conference on Information Sciences and Systems, CISS 2024
ISBN (Electronic)9798350369298
DOIs
StatePublished - 2024
Event58th Annual Conference on Information Sciences and Systems, CISS 2024 - Princeton, United States
Duration: 13 Mar 202415 Mar 2024

Publication series

Name2024 58th Annual Conference on Information Sciences and Systems, CISS 2024

Conference

Conference58th Annual Conference on Information Sciences and Systems, CISS 2024
Country/TerritoryUnited States
CityPrinceton
Period13/03/2415/03/24

Keywords

  • active and passive sensors
  • hybrid measurements
  • least squares
  • nonconvex optimization
  • source localization

Fingerprint

Dive into the research topics of 'Source Localization with Spatially Distributed Active and Passive Sensors'. Together they form a unique fingerprint.

Cite this