Convex relaxation approaches to maximum likelihood DOA estimation in ULA's and UCA's with unknown mutual coupling

Kehu Yang, Shu Cai, Zhi Quan Luo

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

7 Scopus citations

Abstract

Direction of arrival (DOA) estimation using sensor array super-resolution techniques are known to suffer from array modeling errors including array element displacements, mutual coupling, and array gain/phase perturbations. In this paper, we consider maximum likelihood (ML) DOA estimation for multiple sources in the presence of unknown mutual coupling, and propose convex semidefinite relaxation approaches to this nonlinear and non-convex problem for uniform linear arrays (ULA's) and uniform circular arrays (UCA's), respectively. Simulation results show that the proposed method are effective to practical applications.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2556-2559
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • DOA estimation
  • Maximum likelihood
  • UCA
  • ULA
  • semidefinite relaxation

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