Improving the performance of GA-ML DOA estimator with a resampling scheme
Improving the performance of GA-ML DOA estimator with a resampling scheme
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DOI：
10.1016/j.sigpro.2004.06.009
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发表时间：
2004-10-01
影响因子：
4.4
通讯作者：
Lu, YL
中科院分区：
文献类型：
--
作者：
Li, MH;Lu, YL
与其他方法相比，通过遗传算法 (GA) 计算的最大似然 (ML) 到达方向 (DOA) 估计器可提供精确的全局解，具有优越的性能。在本文中，我们提出了一种基于重采样的方案，以提高其解决紧密间隔源的能力，并增强其全局收敛性。为此，基于单个数据集的重采样以并行方式构建多个 GA-ML 估计器，然后将这些估计参与竞争，选择并组合成功的结果以生成更准确的估计。数值研究表明，与包括 GA-ML 在内的一些流行方法相比，该方案提供了更少的 DOA 估计均方根误差 (RMSE)、更高的源分辨率概率和更低的分辨率阈值信噪比 (SNR)；并且该技术对阵列几何形状、源相关性等不敏感。 (C) 2004 Elsevier B.V. 保留所有权利。
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, source correlation, and etc. (C) 2004 Elsevier B.V. All rights reserved.