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CLC number: TN911.72; P733.23

On-line Access: 2021-07-20

Received: 2020-04-20

Revision Accepted: 2020-06-04

Crosschecked: 2020-08-06

Cited: 0

Clicked: 2799

Citations:  Bibtex RefMan EndNote GB/T7714


Xianbin Sun


Xinming Jia


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.7 P.1020-1030


A data-driven method for estimating the target position of low-frequency sound sources in shallow seas

Author(s):  Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang

Affiliation(s):  School of Mechanical and Automobile Engineering, Qingdao University of Technology, Qingdao 266000, China; more

Corresponding email(s):   robin_sun@qut.edu.cn, jiaxinming_123@163.com

Key Words:  Vector hydrophone, Shallow sea, Low frequency, Location estimation, Recurrent neural network

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Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang. A data-driven method for estimating the target position of low-frequency sound sources in shallow seas[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(7): 1020-1030.

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author="Xianbin Sun, Xinming Jia, Yi Zheng, Zhen Wang",
journal="Frontiers of Information Technology & Electronic Engineering",
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%T A data-driven method for estimating the target position of low-frequency sound sources in shallow seas
%A Xianbin Sun
%A Xinming Jia
%A Yi Zheng
%A Zhen Wang
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000181

T1 - A data-driven method for estimating the target position of low-frequency sound sources in shallow seas
A1 - Xianbin Sun
A1 - Xinming Jia
A1 - Yi Zheng
A1 - Zhen Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
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SP - 1020
EP - 1030
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000181

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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