Efficiency Evaluation of the Unconditional Maximum Likelihood Estimator for Near‐Field DOA Estimation

In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA)...

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Published inETRI journal Vol. 28; no. 6; pp. 761 - 769
Main Authors Olague, Jose Arceo, Rosales, David Covarrubias, Rivera, Jose Luna
Format Journal Article
LanguageEnglish
Published 01.12.2006
Subjects
DOA
Near‐field source localization
sensor array
UML estimator
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ISSN1225-6463
2233-7326
DOI10.4218/etrij.06.0106.0006

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Abstract In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near‐field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal‐to‐noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near‐field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple‐source scenarios.
AbstractList In this paper, we address the problem of closely spaced source localization using sensor array processing. In particular, the performance efficiency (measured in terms of the root mean square error) of the unconditional maximum likelihood (UML) algorithm for estimating the direction of arrival (DOA) of near-field sources is evaluated. Four parameters are considered in this evaluation: angular separation among sources, signal-to-noise ratio (SNR), number of snapshots, and number of sources (multiple sources). Simulations are conducted to illustrate the UML performance to compute the DOA of sources in the near-field. Finally, results are also presented that compare the performance of the UML DOA estimator with the existing multiple signal classification approach. The results show the capability of the UML estimator for estimating the DOA when the angular separation is taken into account as a critical parameter. These results are consistent in both low SNR and multiple-source scenarios.
Author Luna‐Rivera, J. M.
Covarrubias‐Rosales, D. H.
Arceo‐Olague, J. G.
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SubjectTerms DOA
Near‐field source localization
sensor array
UML estimator
Title Efficiency Evaluation of the Unconditional Maximum Likelihood Estimator for Near‐Field DOA Estimation
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