一种基于数据驱动的浅海低频声源目标位置估计方法

孙显彬; 贾鑫明; 郑轶; 王振

doi:10.1631/FITEE.2000181

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一种基于数据驱动的浅海低频声源目标位置估计方法

常规文章 | Updated：2022-06-06

- 一种基于数据驱动的浅海低频声源目标位置估计方法
- A data-driven method for estimating the target position of low-frequency sound sources in shallow seas
- 信息与电子工程前沿（英文） 2021年22卷第7期页码：1020-1030
- Affiliations：
  
  School of Mechanical and Automobile Engineering, Qingdao University of Technology, Qingdao 266000, China
  Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qingdao 266000, China
- Author bio：
  
  Xianbin SUN, E-mail: robin_sun@qut.edu.cn
  [ "Xinming JIA, E-mail: jiaxinming_123@163.com" ]
- Funds：
  
  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)
- DOI：10.1631/FITEE.2000181
  中图分类号：
- 收稿：2020-04-20，
  
  修回：2021-;8-;6，
  
  网络出版：2021-01-22，
  
  纸质出版：2021-07
- Accepted：
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孙显彬, 贾鑫明, 郑轶, 等. 一种基于数据驱动的浅海低频声源目标位置估计方法[J]. 信息与电子工程前沿（英文）, 2021,22(7):1020-1030.

Xianbin SUN, Xinming JIA, Yi ZHENG, et al. 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.
孙显彬, 贾鑫明, 郑轶, 等. 一种基于数据驱动的浅海低频声源目标位置估计方法[J]. 信息与电子工程前沿（英文）, 2021,22(7):1020-1030. DOI： 10.1631/FITEE.2000181.

Xianbin SUN, Xinming JIA, Yi ZHENG, et al. 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. DOI： 10.1631/FITEE.2000181.

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。

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.

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references

A Agarwal , , , A Kumar , , , M Agrawal . . Iterative adaptive approach to DOA estimation with acoustic vector sensors . . OCEANS , , 2015 . . p.1 - - 8 . . DOI: 10.1109/OCEANS-Genova.2015.7271605 http://doi.org/10.1109/OCEANS-Genova.2015.7271605 . .

BMR Bharathi , , , AR Mohanty . . Underwater sound source localization by EMD-based maximum likelihood method . . Acoust Aust , , 2018 . . 46 ( ( 2 ): ): 193 - - 203 . . DOI: 10.1007/s40857-018-0129-8 http://doi.org/10.1007/s40857-018-0129-8 . .

T Chai , , , RR Draxler . . Root mean square error (RMSE) or mean absolute error (MAE). -Arguments against avoiding RMSE in the literature . . Geosci Model Dev , , 2014 . . 7 ( ( 3 ): ): 1247 - - 1250 . . DOI: 10.5194/gmd-7-1247-2014 http://doi.org/10.5194/gmd-7-1247-2014 . .

A Chao , , , TJ Shen . . Nonparametric estimation of Shannon's index of diversity when there are unseen species in sample . . Environ Ecol Stat , , 2003 . . 10 ( ( 4 ): ): 429 - - 443 . . DOI: 10.1023/A:1026096204727 http://doi.org/10.1023/A:1026096204727 . .

GL D'Spain , , , JC Luby , , , GR Wilson , , , 等 . . Vector sensors and vector sensor line arrays: comments on optimal array gain and detection . . J Acoust Soc Am , , 2006 . . 120 ( ( 1 ): ): 171 - - 185 . . DOI: 10.1121/1.2207573 http://doi.org/10.1121/1.2207573 . .

HB Demuth , , , MH Beale , , , OD Jess , , , 等 . . Neural Network Design . . Martin Hagan , , 2014 . . .

B Dushaw . . Acoustic Tomography, Ocean . . In: Njoku EG (Ed.), Encyclopedia of Remote Sensing. Springer, New York, USA , , 2014 . . p.4 - - 11 . . DOI: 10.1007/978-0-387-36699-9_211 http://doi.org/10.1007/978-0-387-36699-9_211 . .

P Felisberto , , , P Santos , , , SM Jesus . . Tracking source azimuth using a single vector sensor . . Int Conf on Sensor Technologies and Applications , , 2010 . . p. 416 - - 421 . . DOI: 10.1109/SENSORCOMM.2010.66 http://doi.org/10.1109/SENSORCOMM.2010.66 . .

A Fox , , , C Eichelberger , , , J Hughes , , , 等 . . Spatio-temporal indexing in non-relational distributed databases . . Int Conf on Big Data , , 2013 . . p. 291 - - 299 . . DOI: 10.1109/BigData.2013.6691586 http://doi.org/10.1109/BigData.2013.6691586 . .

JX Gu , , , ZH Wang , , , J Kuen , , , 等 . . Recent advances in convolutional neural networks . . Patt Recogn , , 2018 . . 77 354 - - 377 . . DOI: 10.1016/j.patcog.2017.10.013 http://doi.org/10.1016/j.patcog.2017.10.013 . .

JA Hildebrand . . Anthropogenic and natural sources of ambient noise in the ocean . . Mar Ecol Progr Ser , , 2009 . . 395 5 - - 20 . . DOI: 10.3354/meps08353 http://doi.org/10.3354/meps08353 . .

G Ian , , , B Yoshua , , , C Aaron . . Deep Learning . . The MIT Press, USA , , 2016 . . .

GY Li , , , B Kawan , , , H Wang , , , 等 . . Neural-network-based modelling and analysis for time series prediction of ship motion . . Ship Technol Res , , 2017 . . 64 ( ( 1 ): ): 30 - - 39 . . DOI: 10.1080/09377255.2017.1309786 http://doi.org/10.1080/09377255.2017.1309786 . .

TC Li , , , JY Su , , , W Liu , , , 等 . . Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond . . Front Inform Technol Electron Eng , , 2017 . . 18 ( ( 12 ): ): 1913 - - 1939 . . DOI: 10.1631/FITEE.1700379 http://doi.org/10.1631/FITEE.1700379 . .

R Moussalli , , , M Srivatsa , , , S Asaad . . Fast and flexible conversion of Geohash codes to and from latitude/longitude coordinates . . Proc IEEE 23 rd Annual Int Symp on Field-Programmable Custom Computing Machines , , 2015 . . p. 179 - - 186 . . DOI: 10.1109/FCCM.2015.18 http://doi.org/10.1109/FCCM.2015.18 . .

MB Porter . . The BELLHOP Manual and User's Guide: Preliminary Draft . . Technology Report, USA , , 2011 . . .

T Praczyk . . Using evolutionary neural networks to predict spatial orientation of a ship . . Neurocomputing , , 2015 . . 166 229 - - 243 . . DOI: 10.1016/j.neucom.2015.03.075 http://doi.org/10.1016/j.neucom.2015.03.075 . .

A Prieto , , , B Prieto , , , EM Ortigosa , , , 等 . . Neural networks: an overview of early research, current frameworks and new challenges . . Neurocomputing , , 2016 . . 214 242 - - 268 . . DOI: 10.1016/j.neucom.2016.06.014 http://doi.org/10.1016/j.neucom.2016.06.014 . .

MAJ van Gerven , , , SM Bohte . . Editorial: artificial neural networks as models of neural information processing . . Front Comput Neurosci , , 2017 . . 11 114 DOI: 10.3389/fncom.2017.00114 http://doi.org/10.3389/fncom.2017.00114 . .

D Wittekind , , , M Schuster . . Propeller cavitation noise and background noise in the sea . . Ocean Eng , , 2016 . . 120 116 - - 121 . . DOI: 10.1016/j.oceaneng.2015.12.060 http://doi.org/10.1016/j.oceaneng.2015.12.060 . .

AB Zhao , , , L Ma , , , XF Ma , , , 等 . . An improved azimuth angle estimation method with a single acoustic vector sensor based on an active sonar detection system . . Sensors , , 2017 . . 17 ( ( 2 ): ): 412 DOI: 10.3390/s17020412 http://doi.org/10.3390/s17020412 . .

JB Zhou , , , MH Zhang , , , SC Piao , , , 等 . . Low frequency ambient noise modeling and comparison with field measurements in the South China Sea . . Appl Acoust , , 2019 . . 148 34 - - 39 . . DOI: 10.1016/j.apacoust.2018.11.013 http://doi.org/10.1016/j.apacoust.2018.11.013 . .

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