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.
Original language | English |
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Pages (from-to) | 1813-1822 |
Number of pages | 10 |
Journal | Signal Processing |
Volume | 84 |
Issue number | 10 |
DOIs | |
Publication status | Published - Oct 2004 |
Keywords
- GA–ML DOA estimator
- resampling scheme
- genetic algorithm