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Type: Artigo de periódico
Title: Application of natural computing algorithms to maximum likelihood estimation of direction of arrival
Author: Boccato, L
Krummenauer, R
Attux, R
Lopes, A
Abstract: This work presents a study of the performance of populational meta-heuristics belonging to the field of natural computing when applied to the problem of direction of arrival (DOA) estimation, as well as an overview of the literature about the use of such techniques in this problem. These heuristics offer a promising alternative to the conventional approaches in DOA estimation, as they search for the global optima of the maximum likelihood (ML) function in a framework characterized by an elegant balance between global exploration and local improvement, which are interesting features in the context of multimodal optimization, to which the ML-DOA estimation problem belongs. Thus, we shall analyze whether these algorithms are capable of implementing the ML estimator, i.e., finding the global optima of the ML function. In this work, we selected three representative natural computing algorithms to perform DOA estimation: differential evolution, clonal selection algorithm, and the particle swarm. Simulation results involving different scenarios confirm that these methods can reach the performance of the ML estimator, regardless of the number of sources and/or their nature. Moreover, the number of points evaluated by such methods is quite inferior to that associated with a grid search, which gives support to their application. (C) 2011 Elsevier B.V. All rights reserved.
Subject: Array signal processing
Direction of arrival estimation
Maximum likelihood estimation
Natural computing
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/j.sigpro.2011.12.004
Date Issue: 2012
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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