Moving source depth estimation based on Green's function retrieval with a vector sensor

2017 ◽  
Author(s):  
Zhuang Cheng ◽  
Yanqun Wu ◽  
Zhengliang Hu ◽  
Jun Wang ◽  
Yongming Hu
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Depth Estimation ◽  
Source Depth ◽  
Moving Source ◽  
Vector Sensor

scholarly journals Data‐based differential matched‐mode source depth estimation for a moving source

2019 ◽  
Vol 13 (12) ◽  
pp. 2196-2201
Author(s):  
Junjie Shi ◽  
Dajun Sun ◽  
Qingyu Liu ◽  
Hongling Fu ◽  
Chong Zhao
Keyword(s):  
Depth Estimation ◽  
Source Depth ◽  
Moving Source

scholarly journals A simple method for moving source depth estimation applied to the SWellEx96 data

2021 ◽  
Vol 1 (2) ◽  
pp. 026002
Author(s):  
Jianguo Zhang ◽  
T. C. Yang ◽  
Guangying Zheng
Keyword(s):  
Depth Estimation ◽  
Source Depth ◽  
Simple Method ◽  
Moving Source

scholarly journals Moving Source Depth Estimation Using a Horizontal Line Array in Shallow Water Waveguides

2018 ◽  
Author(s):  
Liang Guolong ◽  
Zhang Yifeng ◽  
Zou Nan ◽  
Wang Jinjin
Keyword(s):  
Shallow Water ◽  
Estimation Method ◽  
Depth Estimation ◽  
Propagation Model ◽  
Small Range ◽  
Horizontal Line ◽  
Source Depth ◽  
Moving Source ◽  
Line Array ◽  
Wavenumber Spectrum

In this study, a matched-mode autoregressive source depth estimation method (MMAR) based on autoregressive (AR) wavenumber estimation is proposed for a moving source in shallow water waveguides. The signal original frequency and the environmental parameters, namely, the sound speed profile and bottom properties are known as a prior knowledge. The mode wavenumbers are estimated by the AR modal wavenumber spectrum. On the basis of the mode wavenumber estimation, the mode amplitudes can be estimated by the wavenumber spectrum that is obtained by generalized Hankel transform. The source depth estimation is determined by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with other methods of moving source depth estimation, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The selection of horizontal line array depth is illustrated by simulation and normal mode theory in details.

scholarly journals Passive broadband source depth estimation in the deep ocean using a single vector sensor

2020 ◽  
Vol 148 (1) ◽  
pp. EL88-EL92
Author(s):  
Yubo Qi ◽  
Shihong Zhou ◽  
Yuquan Liang ◽  
Shuyuan Du ◽  
Changpeng Liu
Keyword(s):  
Deep Ocean ◽  
Depth Estimation ◽  
Source Depth ◽  
Vector Sensor

scholarly journals Match-Mode Autoregressive Method for Moving Source Depth Estimation in Shallow Water Waveguides

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Liang Guo-Long ◽  
Zhang Yi-Feng ◽  
Zou Nan ◽  
Wang Jin-Jin
Keyword(s):  
Shallow Water ◽  
Depth Estimation ◽  
Propagation Model ◽  
Estimation Methods ◽  
Small Range ◽  
Horizontal Line ◽  
Source Depth ◽  
Moving Source ◽  
Line Array ◽  
Wavenumber Spectrum

Source depth estimation is always a problem in underwater acoustic area, because depth estimation is a nonlinear problem. Traditional depth estimation methods use a vertical line array, which has disadvantage of poor mobility due to the size of sensor array. In order to estimate source depth with a horizontal line array, we propose a matched-mode depth estimation method based on autoregressive (AR) wavenumber estimation for a moving source in shallow water waveguides. First, we estimate the mode wavenumbers using the improved AR modal wavenumber spectrum. Second, according to the mode wavenumber estimation, we estimate the mode amplitudes by the wavenumber spectrum, which is obtained by generalized Hankel transform. Finally, we estimate source depth estimation by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with synthetic aperture beamforming, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The robustness of the proposed method is illustrated by simulating the performance in mismatched environment.

GREEN'S FUNCTION MATCHING METHOD FOR REALISTIC CALCULATIONS OF INTERFACES

1985 ◽  
Vol 46 (C4) ◽  
pp. C4-321-C4-329 ◽  
Author(s):  
E. Molinari ◽  
G. B. Bachelet ◽  
M. Altarelli
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Matching Method

THE ATOMISTIC GREEN'S FUNCTION METHOD FOR INTERFACIAL PHONON TRANSPORT

2014 ◽  
Vol 17 (N/A) ◽  
pp. 89-145 ◽  
Author(s):  
Sridhar Sadasivam ◽  
Yuhang Che ◽  
Zhen Huang ◽  
Liang Chen ◽  
Satish Kumar ◽  
...  
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Function Method ◽  
Phonon Transport ◽  
Green’S Function Method
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