基于SOM和BP神经网络的声阵列声源到达方向估计

Baoliang Sun, C. Jiang, Yuguang Song, K. Xue, Weike Shi
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引用次数: 0

摘要

提出了一种基于自组织特征映射(SOM)和反向传播神经网络(BPNN)的声阵列声源到达方向(DOA)估计算法。该算法基于到达时间差(TDOA),将具有相似拓扑结构的TDOA向量映射到同一空间区域,得到该空间区域的特征TDOA向量。将该特征TDOA向量输入到BPNN中进行求解,从而得到DOA估计。通过对由5个传感器组成的矩形金字塔阵的声定位分析,找出了声阵的盲区,该盲区声阵的声定位误差急剧增大。然而,所提出的DOA估计算法能够分离盲区和可探测区,提高不同区域声源的DOA估计精度。仿真测试和实际实验表明,该算法具有较高的DOA估计精度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Direction-of-Arrival Estimation of Acoustic Sources Using Acoustic Array Based on SOM and BP Neural Network
Abstract-A direction-of-arrival (DOA) estimation algorithm of acoustic sources using acoustic array based on self-organizing feature map (SOM) and back propagation neural networks (BPNN) was proposed in this paper. Based on time difference of arrival (TDOA), this algorithm maps TDOA vectors with similar topology into one spatial zone, and gets the characteristic TDOA vector of this spatial zone. This characteristic TDOA vector will be input into BPNN for settlement, thus getting the DOA estimation. The blind zone of array was identified by analyzing sound localization of a rectangular pyramid array of five sensors, in which sound localization error of the acoustic array increased dramatically. However, the proposed DOA estimation algorithm can separate the blind zone and detectable zone, improving DOA estimation accuracy of acoustic sources in different regions. The simulation test and actual experiment demonstrated that the algorithm has high DOA estimation accuracy and robustness.
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