Source depth estimation based on synthetic aperture beamfoming for a moving source

2015 ◽  
Vol 138 (3) ◽  
pp. 1678-1686 ◽  
Author(s):  
T. C. Yang
Keyword(s):  
Depth Estimation ◽  
Synthetic Aperture ◽  
Source Depth ◽  
Moving Source

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 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.

Source depth estimation using multi-station waveform data

1984 ◽  
Vol 74 (5) ◽  
pp. 1623-1643
Author(s):  
Falguni Roy
Keyword(s):  
Depth Estimation ◽  
The United States ◽  
Digital Data ◽  
United States Geological Survey ◽  
Travel Times ◽  
Source Depth ◽  
Time Data ◽  
Waveform Data ◽  
Level Ii ◽  
Level I

Abstract A depth estimation procedure has been described which essentially attempts to identify depth phases by analyzing multi-station waveform data (hereafter called level II data) in various ways including deconvolution, prediction error filtering, and spectral analysis of the signals. In the absence of such observable phases, other methods based on S-P, ScS-P, and SKS-P travel times are tried to get an estimate of the source depth. The procedure was applied to waveform data collected from 31 globally distributed stations for the period between 1 and 15 October 1980. The digital data were analyzed at the temporary data center facilities of the National Defense Research Institute, Stockholm, Sweden. During this period, a total number of 162 events in the magnitude range 3.5 to 6.2 were defined by analyzing first arrival time data (hereafter called level I data) alone. For 120 of these events, it was possible to estimate depths using the present procedure. The applicability of the procedure was found to be 100 per cent for the events with mb > 4.8 and 88 per cent for the events with mb > 4. A comparison of level I depths and level II depths (the depths as obtained from level I and level II data, respectively) with that of the United States Geological Survey estimates indicated that it will be necessary to have at least one local station (Δ < 10°) among the level I data to obtain reasonable depth estimates from such data alone. Further, it has been shown that S wave travel times could be successfully utilized for the estimation of source depth.

Formulas for Source Depth Estimation From Multipath Arrivals in Deep Water

2020 ◽  
Vol 56 (6) ◽  
pp. 4856-4871
Author(s):  
Rui Duan ◽  
Kunde Yang ◽  
Feiyun Wu ◽  
Yuanliang Ma
Keyword(s):  
Deep Water ◽  
Depth Estimation ◽  
Source Depth

scholarly journals Striation-based source depth estimation with a vertical line array in the deep ocean

2018 ◽  
Vol 143 (1) ◽  
pp. EL8-EL12 ◽  
Author(s):  
Kunde Yang ◽  
Liya Xu ◽  
Qiulong Yang ◽  
Rui Duan
Keyword(s):  
Deep Ocean ◽  
Depth Estimation ◽  
Vertical Line ◽  
Source Depth ◽  
Line Array
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