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A PARALIND Decomposition-Based Coherent Two-Dimensional Direction of Arrival Estimation Algorithm for Acoustic Vector-Sensor Arrays

机译:声矢量传感器阵列的基于PARALIND分解的相干二维到达方向估计算法

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In this paper, we combine the acoustic vector-sensor array parameter estimation problem with the parallel profiles with linear dependencies (PARALIND) model, which was originally applied to biology and chemistry. Exploiting the PARALIND decomposition approach, we propose a blind coherent two-dimensional direction of arrival (2D-DOA) estimation algorithm for arbitrarily spaced acoustic vector-sensor arrays subject to unknown locations. The proposed algorithm works well to achieve automatically paired azimuth and elevation angles for coherent and incoherent angle estimation of acoustic vector-sensor arrays, as well as the paired correlated matrix of the sources. Our algorithm, in contrast with conventional coherent angle estimation algorithms such as the forward backward spatial smoothing (FBSS) estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, not only has much better angle estimation performance, even for closely-spaced sources, but is also available for arbitrary arrays. Simulation results verify the effectiveness of our algorithm.
机译:在本文中,我们将声矢量传感器阵列参数估计问题与具有线性相关性的平行轮廓(PARALIND)模型相结合,该模型最初应用于生物学和化学领域。利用PARALIND分解方法,我们针对受未知位置影响的任意间隔声学矢量传感器阵列提出了一种盲相干二维到达方向(2D-DOA)估计算法。所提出的算法很好地实现了自动配对的方位角和仰角,以进行声矢量传感器阵列的相干和非相干角度估计,以及源的成对相关矩阵。与传统的相干角度估计算法(例如通过旋转不变技术(ESPRIT)算法对信号参数进行前向后向空间平滑(FBSS)估计)相比,我们的算法不仅具有更好的角度估计性能,即使对于间隔很近的信号源,但也可用于任意数组。仿真结果验证了该算法的有效性。

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