An Efficient Framework for Estimating the Direction of Multiple Sound Sources Using Higher-Order Generalized Singular Value Decomposition
An Efficient Framework for Estimating the Direction of Multiple Sound Sources Using Higher-Order Generalized Singular Value Decomposition
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DOI：
10.3390/s19132977
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Bandhit Suksiri;M. Fukumoto
中科院分区：
文献类型：
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作者：
Bandhit Suksiri;M. Fukumoto
本文提出了一种有效的宽带声源波达方向（DOA）估计框架。所提出的框架提供了一种有效的方法来构建一个宽带互相关矩阵从多个窄带互相关矩阵的所有频率箱。此外，该方法还受到相干信号子空间技术的启发，进一步改进了线性变换过程，利用接收信号与其自身在不同频率上的互相关矩阵，沿着对该互相关矩阵进行高阶广义奇异值分解，不再需要任何DOA初步估计过程。宽带DOA估计采用任何基于子空间的技术估计窄带DOA，但使用建议的宽带相关性，而不是窄带相关矩阵。这意味着，该框架使最近的窄带子空间方法的前沿研究直接估计宽带源的DOA，从而降低计算复杂度，并促进估计算法。最后通过实例验证了该方法的适用性和有效性，结果表明，在一定信噪比范围内，融合方法的性能优于其他方法，且传感器数目较少，适合于实际应用。
This paper presents an efficient framework for estimating the direction-of-arrival (DOA) of wideband sound sources. The proposed framework provides an efficient way to construct a wideband cross-correlation matrix from multiple narrowband cross-correlation matrices for all frequency bins. In addition, the proposed framework is inspired by the coherent signal subspace technique with further improvement of linear transformation procedure, and the new procedure no longer requires any process of DOA preliminary estimation by exploiting unique cross-correlation matrices between the received signal and itself on distinct frequencies, along with the higher-order generalized singular value decomposition of the array of this unique matrix. Wideband DOAs are estimated by employing any subspace-based technique for estimating narrowband DOAs, but using the proposed wideband correlation instead of the narrowband correlation matrix. It implies that the proposed framework enables cutting-edge studies in the recent narrowband subspace methods to estimate DOAs of the wideband sources directly, which result in reducing computational complexity and facilitating the estimation algorithm. Practical examples are presented to showcase its applicability and effectiveness, and the results show that the performance of fusion methods perform better than others over a range of signal-to-noise ratios with just a few sensors, which make it suitable for practical use.