Abstract
Estimating the target position of low-frequency sound sources in a shallow sea environment is difficult due to the high cost of hydrophone placement and the complexity of the propagation model. We propose a compressed recurrent neural network (C-RNN) model that compresses the signal received by a vector hydrophone into a dynamic sound intensity signal and compresses the target position of the sound source into a GeoHash code. Two types of data are used to carry out prior training on the recurrent neural network, and the trained network is subsequently used to estimate the target position of the sound source. Compared with traditional mathematical models, the C-RNN model functions independently under the complex sound field environment and terrain conditions, and allows for real-time positioning of the sound source under low-parameter operating conditions. Experimental results show that the average error of the model is 56 m for estimating the target position of a low-frequency sound source in a shallow sea environment.
摘要
由于水听器的布置成本高且水下声音传播模型复杂,在浅海环境中进行低频声源目标位置估计较为困难。提出一种基于数据驱动的压缩循环神经网络(compressed recurrent neural network,C-RNN)模型。该模型首先将矢量水听器接收到的声源信号压缩为动态声强信号,然后将声源位置进行GeoHash编码用于该模型的先验训练,最后使用训练好的模型进行浅海低频声源目标的位置估计。与传统数学模型相比,所提C-RNN模型能在复杂声场环境和地形条件下以低参数工况实时估计声源位置。实验结果表明,该模型对浅海环境中低频声源目标位置的平均定位精度为56米。
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Xianbin SUN designed the research. Xinming JIA and Zhen WANG processed the data. Xinming JIA drafted the manuscript. Xianbin SUN and Yi ZHENG helped organize the manuscript. Xianbin SUN and Xinming JIA revised and finalized the paper.
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Xianbin Sun, Xinming JIA, Yi ZHENG, and Zhen WANG declare that they have no conflict of interest.
Project supported by the National Natural Science Foundation of China (No. 51475249) and the Key Research and Development Program of Shandong Province, China (No. 2018GGX103016)
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Sun, X., Jia, X., Zheng, Y. et al. A data-driven method for estimating the target position of low-frequency sound sources in shallow seas. Front Inform Technol Electron Eng 22, 1020–1030 (2021). https://doi.org/10.1631/FITEE.2000181
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DOI: https://doi.org/10.1631/FITEE.2000181