Improving the performance of GA-ML DOA estimator with a resampling scheme

M. Li, Y. Lu

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

The maximum likelihood (ML) direction of arrival (DOA) estimator computed by genetic algorithm (GA) for the exact global solution gives a superior performance compared to other methods. In this paper, we present a resampling-based scheme to improve its ability to resolve closely spaced sources, and to enhance its global convergence. For this purpose, multiple GA–ML estimators are constructed in a parallel manner based on resampling of a single data set, then those estimates are involved into a competition, and successful results are selected and combined to generate a more accurate estimate. Numerical studies demonstrate that the proposed scheme provides less DOA estimation root-mean-squared error (RMSE), higher source resolution probability and lower resolution threshold signal-to-noise ratio (SNR) than some popular approaches including GA–ML; and this technique is not sensitive to the array geometry.
LanguageEnglish
Pages1813-1822
Number of pages10
JournalSignal Processing
Volume84
Issue number10
DOIs
Publication statusPublished - Oct 2004

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Direction of arrival
Maximum likelihood
Genetic algorithms
Signal to noise ratio
Geometry

Keywords

  • GA–ML DOA estimator
  • resampling scheme
  • genetic algorithm

Cite this

Li, M. ; Lu, Y. / Improving the performance of GA-ML DOA estimator with a resampling scheme. In: Signal Processing. 2004 ; Vol. 84, No. 10. pp. 1813-1822.
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Improving the performance of GA-ML DOA estimator with a resampling scheme. / Li, M.; Lu, Y.

In: Signal Processing, Vol. 84, No. 10, 10.2004, p. 1813-1822.

Research output: Contribution to journalArticle

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