Presentation 2008-06-12
Irreducible Form of AM Criterion for Unconditional ML Estimation of DOA
Haihua CHEN, Masakiyo SUZUKI,
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Abstract(in English) This paper addresses the issue of numerical unstability of Alternating Minimization (AM) criterion in Unconditional Maximum Likelihood (UML) estimation of Directions-of-Arrival (DOA). First we show the definition of the exact UML estimation and brief description of the AM method to solve the exact UML estimation. The AM algorithm becomes numerical unstable, when more than one direction parameters are going to have an identical value. It is caused by the fact that the UML criterion with duplicated directions is indefinite. This paper derives efficient AM (EAM) algorithm by dividing the UML criterion into two components. One depends on a signal variable parameter and the other does not. Then applying the condition of the uniform linear array of sensors, an irreducible form of the criterion of EAM algorithm is derived. We call it Irreducible AM (IAM) algorithm. Finally, simulation results are shown to demonstrate that IAM algorithm is numerical stable and efficient.
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Keyword(in English) DOA finding / Stochastic signal model / ML / AM / Numerical unstability / Irreducible from
Paper # SIS2008-7
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Conference Date 2008/6/5(1days)
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Title (in Japanese) (See Japanese page)
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Title (in English) Irreducible Form of AM Criterion for Unconditional ML Estimation of DOA
Sub Title (in English)
Keyword(1) DOA finding
Keyword(2) Stochastic signal model
Keyword(3) ML
Keyword(4) AM
Keyword(5) Numerical unstability
Keyword(6) Irreducible from
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 2008-06-12
Paper # SIS2008-7
Volume (vol) vol.108
Number (no) 85
Page pp.pp.-
#Pages 6
Date of Issue