Document
type: Class

Continuant
type: Class

Entity
type: Class

Generically Dependent Continuant
type: Class

RDF description of Efficiency evaluation of the unconditional maximum likelihood estimator for near-field DOA estimation - http://orbis.uaslp.mx/vivo/individual/scopus1661
rights: termsOfUse
date: 2026-08-25T14:37:43
publisher: vivo
type: Document

ETRI Journal
type: Thing
publication venue for: Efficiency evaluation of the unconditional maximum likelihood estimator for near-field DOA estimation
type: Collection
type: Generically Dependent Continuant
type: Document
type: Continuant
type: Journal
type: Entity
type: Information Content Entity
type: Periodical

Article
type: Class

Information Content Entity
type: Class

Efficiency evaluation of the unconditional maximum likelihood estimator for near-field DOA estimation
has publication venue: ETRI Journal
type: Generically Dependent Continuant
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.
type: Document
volume: 28
related by: authorship 9928
estado: published
page start: 761
date time value: article date 1662
page end: 769
type: Thing
accesstype: All Open Access, Bronze
doi: 10.4218/etrij.06.0106.0006
most specific type: Document
type: Continuant
related by: authorship 9927
related by: authorship 9926
type: Entity
most specific type: Article
e iscopus: 2-s2.0-33845387131
crisid: 1662
freetext keyword: 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
origen: scopus
type: Article
type: Information Content Entity
issue: 6