This paper proposes a new acoustic vector sensor array geometry containing a sparse right triangular subarray with three guiding vector sensors plus an additional subarray with arbitrarily placed vector sensors at unknown locations and develops a new cumulant-based algorithm for two-dimensional direction estimation (CADE) of multiple non-Gaussian signals. In the CADE algorithm, three cumulant matrices are defined to form a third-order tensor, using which the direction cosines of the signal are recovered by utilizing parallel factor (PARAFAC) fitting. Furthermore, the PARAFAC model is extended to multiple cumulant matrices and a fourth-order tensor framework to enhance the identifiability issues and to improve the performance of the algorithm. Unlike most of the existing acoustic vector sensor direction estimation algorithms, the proposed algorithm imposes less geometric constraint on the array shape and requires no open-form two-dimensional searching and parameter pairing. Simulation results demonstrate the superiority of the CADE algorithm.

中文翻译：

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使用声矢量传感器阵列的基于累积量的二维方向估计

本文提出了一种新的声学矢量传感器阵列几何结构，其中包含一个带有三个引导矢量传感器的稀疏直角三角形子阵列以及一个在未知位置任意放置矢量传感器的附加子阵列，并开发了一种新的基于累积量的二维方向估计算法 (CADE)多个非高斯信号。在CADE算法中，定义了三个累积矩阵形成一个三阶张量，利用该张量通过并行因子（PARAFAC）拟合来恢复信号的方向余弦。此外，PARAFAC 模型被扩展到多个累积矩阵和四阶张量框架，以增强可识别性问题并提高算法的性能。与大多数现有的声学矢量传感器方向估计算法不同，所提出的算法对阵列形状施加较少的几何约束，并且不需要开放形式的二维搜索和参数配对。仿真结果证明了CADE算法的优越性。