The flexibility needed to construct DoA estimators that can be used with rotating arrays subject to rapid variations of the signal frequency is offered by the stochastic maximum likelihood approach. Using a combination of analytic methods and Monte Carlo simulations, we show that for low and moderate source correlations the stochastic maximum likelihood estimator that assumes noncorrelated sources has accuracy comparable to the estimator that includes the correlation coefficient as one of the parameters. We propose several fast approximations of the stochastic maximum likelihood estimator and compare their accuracy with the Crámer-Rao lower bound. We also discuss the model order selection problem for the binary- and multiple-hypotheses cases.

中文翻译：

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基于随机最大似然法的旋转阵列DoA估计

随机最大似然方法提供了构建 DoA 估计器所需的灵活性，该估计器可与受信号频率快速变化影响的旋转阵列一起使用。使用分析方法和蒙特卡罗模拟的组合，我们表明对于低和中等源相关性，假设不相关源的随机最大似然估计器具有与将相关系数作为参数之一的估计器相媲美的准确性。我们提出了随机最大似然估计量的几种快速近似，并将它们的准确性与 Cramer-Rao 下界进行比较。我们还讨论了二元和多假设情况下的模型顺序选择问题。