Conventional direction-of-arrival (DOA) estimation algorithms for shallow water environments usually contain high amounts of error due to the presence of many acoustic reflective surfaces and scattering fields. Utilizing data from a single acoustic vector sensor, the magnitude and DOA of an acoustic signature can be estimated; as such, DOA algorithms are used to reduce the error in these estimations. Three experiments were conducted using a moving boat as an acoustic target in a waterway in Houghton, Michigan. The shallow and narrow waterway is a complex and non-linear environment for DOA estimation. This paper compares minimizing DOA errors using conventional and machine learning algorithms. The conventional algorithm uses frequency-masking averaging, and the machine learning algorithms incorporate two recurrent neural network architectures, one shallow and one deep network. Results show that the deep neural network models the shallow water environment better than the shallow neural network, and both networks are superior in performance to the frequency-masking average method.

中文翻译：

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使用矢量传感器识别浅水通道声源到达方向的循环网络

由于存在许多声反射表面和散射场，用于浅水环境的传统到达方向 (DOA) 估计算法通常包含大量误差。利用来自单个声学矢量传感器的数据，可以估计声学特征的幅度和 DOA；因此，DOA 算法用于减少这些估计中的误差。在密歇根州霍顿的一条水道中，使用移动的船作为声学目标进行了三个实验。浅而窄的水道是DOA估计的复杂非线性环境。本文比较了使用传统算法和机器学习算法最小化 DOA 误差。传统算法使用频率掩蔽平均，机器学习算法包含两个循环神经网络架构，一浅一深网络。结果表明，深度神经网络比浅层神经网络更好地模拟浅水环境，并且两种网络在性能上都优于频率掩蔽平均法。