Du Lin, Xu Luzhou, Li Jian, Guo Bin, Stoica Petre, Bahr Chris, Cattafesta Louis N
Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida 32611, USA.
J Acoust Soc Am. 2010 Nov;128(5):2877-87. doi: 10.1121/1.3489112.
In this paper, several covariance-based approaches are proposed for aeroacoustic noise source analysis under the assumptions of a single dominant source and all observers contaminated solely by uncorrelated noise. The Cramér-Rao Bounds (CRB) of the unbiased source power estimates are also derived. The proposed methods are evaluated using both simulated data as well as data acquired from an airfoil trailing edge noise experiment in an open-jet aeroacoustic facility. The numerical examples show that the covariance-based algorithms significantly outperform an existing least-squares approach and provide accurate power estimates even under low signal-to-noise ratio (SNR) conditions. Furthermore, the mean-squared-errors (MSEs) of the so-obtained estimates are close to the corresponding CRB especially for a large number of data samples. The experimental results show that the power estimates of the proposed approaches are consistent with one another as long as the core analysis assumptions are obeyed.
本文提出了几种基于协方差的方法，用于在单个主导声源以及所有观测器仅受不相关噪声污染的假设下进行气动声学噪声源分析。还推导了无偏源功率估计的克拉美罗界（CRB）。使用模拟数据以及从开放式喷气式气动声学设施中的翼型后缘噪声实验获取的数据对所提出的方法进行了评估。数值示例表明，基于协方差的算法显著优于现有的最小二乘法，并且即使在低信噪比（SNR）条件下也能提供准确的功率估计。此外，如此获得的估计值的均方误差（MSE）尤其对于大量数据样本而言接近相应的CRB。实验结果表明，只要遵循核心分析假设，所提出方法的功率估计彼此一致。
