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Generically Dependent Continuant
RDF description of Efficiency evaluation of the unconditional maximum likelihood estimator for near-field DOA estimation - http://orbis.uaslp.mx/vivo/individual/scopus1661
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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.
Efficiency evaluation of the unconditional maximum likelihood estimator for near-field DOA estimation
DOA; Near-field source localization; Sensor array; UML estimator Algorithms; Computer simulation; Parameter estimation; Problem solving; Sensors; Signal processing; Signal to noise ratio; Direction of arrival (DOA); Near field source localization; Sensor arrays; UML estimators; Maximum likelihood estimation