Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/88758
Type: Artigo de periódico
Title: Maximum Likelihood-based Direction-of-arrival Estimator For Discrete Sources
Author: Krummenauer R.
Ferrari R.
Suyama R.
Attux R.
Junqueira C.
Larzabal P.
Forster P.
Lopes A.
Abstract: This paper addresses the problem of direction-of-arrival (DOA) parameter estimation in array processing when the signals are inherently discrete, which is the case mainly in the digital communication context. Based on the particular structure of the signal space in the data model, a maximum likelihood-based approach is introduced. The strategy consists in transforming the parameter estimation problem into a decision task. It is shown through numerical simulations that the proposed solution closely follows the performance limit given by the Cramér-Rao bound. Some important features of the technique are as follows: (i) it is capable of handling any number of sources, provided that the number of sensors is greater than or equal to two and the number of snapshots is sufficiently greater than the cardinality of the signal space; (ii) the estimation quality is not affected by the angle and phase separation; and (iii) it offers the possibility to deal with uncalibrated arrays. © 2013 Springer Science+Business Media New York.
Editor: 
Rights: fechado
Identifier DOI: 10.1007/s00034-013-9583-6
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84883354504&partnerID=40&md5=a2bd1badf02952bc338cfbd9cfa41463
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

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