Mejora de la Resolución de Fuentes en Campo Cercano por Medio del Estimador UML (Near Field Source Separation Improvement Through Unconditional Maximum Likelihood Estimator)

Dario Bonilla Hernández (dbonilla@cicese.mx)1, David Covarrubias Rosales (dacoro@cicese.mx)1, Jose Arceo Olague (arceojg@citedi.mx)2


1CICESE research center Electronics and Communications Department, Wireless Communications Group (WCG), Ensenada, BC 22860 México
2CITEDI Research Center, Tijuana, BC 22500 México - CICESE research center Electronics and Communications Department, Wireless Communications Group (WCG), Ensenada, BC 22860 México

This paper appears in: Revista IEEE América Latina

Publication Date: Dec. 2006
Volume: 4,   Issue: 6 
ISSN: 1548-0992


Abstract:
In this work, we are focused in the improvement of the near field source separation through the approach of the Unconditional Maximum Likelihood (UML) estimator. Four aspects are considered: separation sources, SNR variation, snapshots and multiple sources, in order to evaluate their influence in the capacity separation for the case of closely spaced sources. In this way, we can establish the minimum conditions for the sources separation. In addition, we investigated the effects of snapshots and the increasing number of sources in their spatial position estimation. Using Monte Carlo simulation, we obtained the Root Mean Square (RMS) error of the source’s direction of arrival. For evaluation purposes we include also MUSIC simulations. Our results show that the UML estimator improves source’s separation performance under low SNR and snapshot values as well as increasing number of sources.

Index Terms:
Near Field, DOA, UML estimator, closely-spaced, source separability   


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