Presentation 2009-06-11
Exact Formulation of Stochastic ML Extimation of DOA
Masakiyo SUZUKI, Hihua CHEN,
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Abstract(in English) This paper presents an exact formulation of Stochastic or Unconditional Maximum Likelihood (UML) estimation for directions-of-arrival (DOA) finding. In the previous formulation, an important condition is missing. That is the non-negative definiteness of the covariance matrix of signal components without additive noises. Because of this, it is not only guaranteed that the covariance matrix is non-negative definite, but also inadequate global solutions appear in the solution space. Furthermore global search fails in finding an adequate solution. This paper proposes an exact formulation of UML which guarantees to estimete a non-negative definite covariance matrix of signal components. Then the inadequate global solutions disappear and global search succeeds in finding adequate solution.
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Keyword(in English) DOA finding / ML estimation / stochastic signal model / non-negative definiteness
Paper # SIS2009-2
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Committee SIS
Conference Date 2009/6/4(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Exact Formulation of Stochastic ML Extimation of DOA
Sub Title (in English)
Keyword(1) DOA finding
Keyword(2) ML estimation
Keyword(3) stochastic signal model
Keyword(4) non-negative definiteness
1st Author's Name Masakiyo SUZUKI
1st Author's Affiliation Kitami Institute of Technology()
2nd Author's Name Hihua CHEN
2nd Author's Affiliation Kitami Institute of Technology
Date 2009-06-11
Paper # SIS2009-2
Volume (vol) vol.109
Number (no) 78
Page pp.pp.-
#Pages 6
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