Presentation 2007-09-28
Global and Local Solutions to Stochastic ML Estimation of DOA
Haihua CHEN, Masakiyo SUZUKI,
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Abstract(in English) This paper addresses the issue of uniqueness of Stochastic Maximum Likelihood (SML) estimation for directions-of-arrival (DOA) finding. Global search techniques for the SML estimation fail to find DOA in many cases, unlike the Deterministic ML (DML) estimation. Based on investigating properties of global and local solutions, this paper reveals that its reason lies in the non-uniqueness of the SML estimation. Based on the idea that the SML local solution closest to the DML solution can be considered to be the most adequate for DOA finding, we propose an algorithm which uses a local search of the SML criterion together with the DML estimation as initialization. Finally some simulation results are shown to demonstrate the proposed algorithm is effective.
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Keyword(in English) DOA finding / Stochasric signal model / Deterministic signal model / ML / Uniqueness
Paper # SIP2007-98,SIS2007-34,SP2007-60
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Committee SIS
Conference Date 2007/9/21(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Global and Local Solutions to Stochastic ML Estimation of DOA
Sub Title (in English)
Keyword(1) DOA finding
Keyword(2) Stochasric signal model
Keyword(3) Deterministic signal model
Keyword(4) ML
Keyword(5) Uniqueness
1st Author's Name Haihua CHEN
1st Author's Affiliation Graduate School of Engineering, Kitami Institute of Technology()
2nd Author's Name Masakiyo SUZUKI
2nd Author's Affiliation Graduate School of Engineering, Kitami Institute of Technology
Date 2007-09-28
Paper # SIP2007-98,SIS2007-34,SP2007-60
Volume (vol) vol.107
Number (no) 237
Page pp.pp.-
#Pages 6
Date of Issue