为了解决非均匀噪声情况下声矢量传感器阵列方位估计性能恶化的问题，提出了基于加权最小二乘(Weighted Least Squares, WLS)的稀疏信号重构方法和基于加权协方差矩阵拟合(Weighted Covariance Matrix Fitting, WCMF)的稀疏信号重构方法。首先，定义了一个虚拟的声矢量传感器阵列流形矩阵，并重构包含稀疏信号功率和噪声功率的协方差矩阵。然后，为了估计稀疏信号功率和每个通道输出的噪声功率，基于WLS方法和稀疏信号加权最小化方法，构造了关于稀疏信号功率和噪声功率的代价函数。在此基础上，为了进一步提高稀疏信号功率和噪声功率的估计精度，基于WCMF准则对构造的代价函数进行改进。最后，应用泰勒级数展开式将关于稀疏信号功率和噪声功率的非线性代价函数转化为线性函数，并采用循环迭代算法估计稀疏信号功率和噪声功率。待迭代终止时，对稀疏信号功率谱峰搜索，即可实现对目标的方位估计。仿真结果表明，与现有非均匀噪声下的估计方法相比，所提方法提高了非均匀噪声情况下声矢量传感器阵列的方位估计精度。
Abstract
To improve the direction of arrival (DOA) estimation performance of the acoustic vector sensor array in the presence of non-uniform noise, the sparse signal reconstruction methods based on weighted least squares (WLS) and weighted covariance matrix fitting (WCMF) are proposed in this paper. Firstly, a virtual manifold matrix of acoustic vector sensor array is defined, and the covariance matrix containing the sparse signal power and the noise power is reconstructed. To estimate the sparse signal power and the output noise power of each channel for the acoustic vector sensor array, a cost function via the sparse signal power and the noise power is then formulated based on the WLS method and the sparse signal weighted minimization method. On this basis, to further improve the estimation accuracy of sparse signal power and noise power, the formulated cost function is improved based on the WCMF criterion. Finally, the nonlinear cost function is transformed into a linear function by using the Taylor series expansion, then the sparse signal power and the noise power are estimated by loop iterative algorithm. Furthermore, the spectral peak search is performed on the sparse signal power to achieve the DOA estimation of sources when the iteration is terminated. Simulation results show that the proposed methods achieve a more accurate DOA estimation compared to the existing non-uniform noise power estimation methods.
关键词
声矢量传感器阵列 /
非均匀噪声 /
稀疏重构 /
方位估计
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Key words
Acoustic vector sensor array /
non-uniform noise /
sparse reconstruction /
direction of arrival estimation
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