Semidefinite programming approach for range-difference based source localization

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

90 Scopus Citations
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Author(s)

  • Kenneth W.K. Lui
  • Frankie Kit Wing Chan
  • H. C. So

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1630-1633
Journal / PublicationIEEE Transactions on Signal Processing
Volume57
Issue number4
Publication statusPublished - 2009

Abstract

A common technique for passive source localization is to utilize the range-difference (RD) measurements between the source and several spatially separated sensors. The RD information defines a set of hyperbolic equations from which the source position can be calculated with the knowledge of the sensor positions. Under the standard assumption of Gaussian distributed RD measurement errors, it is well known that the maximum-likelihood (ML) position estimation is achieved by minimizing a multimodal cost function which corresponds to a difficult task. In this correspondence, we propose to approximate the nonconvex ML optimization by relaxing it to a convex optimization problem using semidefinite programming. A semidefinite relaxation RD-based positioning algorithm, which makes use of the admissible source position information, is proposed and its estimation performance is contrasted with the two-step weighted least squares method and nonlinear least squares estimator as well as Cramér-Rao lower bound. © 2009 IEEE.

Research Area(s)

  • Range-difference measurements, Semidefinite programming, Source localization, Time-delay estimation

Citation Format(s)

Semidefinite programming approach for range-difference based source localization. / Lui, Kenneth W.K.; Chan, Frankie Kit Wing; So, H. C.
In: IEEE Transactions on Signal Processing, Vol. 57, No. 4, 2009, p. 1630-1633.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review