Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/69293
Type: Artigo de periódico
Title: Improving the Efficiency of Natural Computing Algorithms in DOA Estimation Using a Noise Filtering Approach
Author: Boccato, L
Krummenauer, R
Attux, R
Lopes, A
Abstract: We propose a novel strategy to generate initial candidate solutions for bio-inspired algorithms applied to the direction of arrival estimation problem. The idea, which aims to improve the efficiency of the estimator, consists in using the frequency response of a well-known optimum noise reduction filter as the probability density function of the set of candidate solutions. In accordance to this approach, we also employ a modified likelihood function to reduce the estimation error. Simulation results considering an immune-inspired algorithm confirm a significant improvement of its performance and efficiency, and the new estimator reaches the conditional Cram,r-Rao lower bound.
Subject: Direction of arrival estimation
Maximum likelihood estimator
Optimization
Noise filtering
Natural computing
Country: EUA
Editor: Springer Birkhauser
Rights: fechado
Identifier DOI: 10.1007/s00034-012-9538-3
Date Issue: 2013
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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