Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867062
Xionghou Liu, Chi Zhang, Hongyu Chen, Chao Sun, Yixin Yang, Kuan Fan, Jiapeng Liu
The three-dimensional imaging sonar using a planar or volumetric hydrophone array is useful for underwater acoustic imaging, since it can provide the volumetric information of an underwater target. However, the system cost is at a high level due to the large number of hydrophones adopted. To reduce the system cost, the sparse array optimization is often used to remove a number of hydrophones and simultaneously, to keep a desired beampattern performance. Nevertheless, the angle resolution of a sparse optimized hydrophone array is not enough for relatively long range imaging application, such as the small target detection. Different from the sparse array optimization method, using a forward-looking sonar composed of several horizontally paralleled uniform linear arrays (ULAs) can achieve a good 3-D imaging ability. The 3-D forward-looking sonar is based on the slant looking imaging processing, and it combines the conventional horizontal beamforming and the high-resolution vertical beamforming together. Nevertheless, the horizontal angle resolution is restricted by the limited physical size of the sonar platform (e.g., an underwater manned vehicle or an underwater robot). What is worse, the restricted angle resolution in the horizontal direction will degrade the imaging performance of the vertical beamforming. To solve the problem, we design a MIMO sonar array layout for the 3-D forward-looking imaging. The designed MIMO sonar array is composed of two sparsely located transmitting transducers and several parallel ULAs. By doing so, the horizontal angle resolution doubles that of a traditional sonar array (which is composed of one transmitter and several paralleled ULAs), and the vertical imaging ability is substantially improved. We give an example of the MIMO sonar array, and give the imaging performance analysis to validate the effectiveness of the designed array layout.
{"title":"An MIMO Sonar Array for High-resolution 3D Forward-looking Imaging","authors":"Xionghou Liu, Chi Zhang, Hongyu Chen, Chao Sun, Yixin Yang, Kuan Fan, Jiapeng Liu","doi":"10.1109/OCEANSE.2019.8867062","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867062","url":null,"abstract":"The three-dimensional imaging sonar using a planar or volumetric hydrophone array is useful for underwater acoustic imaging, since it can provide the volumetric information of an underwater target. However, the system cost is at a high level due to the large number of hydrophones adopted. To reduce the system cost, the sparse array optimization is often used to remove a number of hydrophones and simultaneously, to keep a desired beampattern performance. Nevertheless, the angle resolution of a sparse optimized hydrophone array is not enough for relatively long range imaging application, such as the small target detection. Different from the sparse array optimization method, using a forward-looking sonar composed of several horizontally paralleled uniform linear arrays (ULAs) can achieve a good 3-D imaging ability. The 3-D forward-looking sonar is based on the slant looking imaging processing, and it combines the conventional horizontal beamforming and the high-resolution vertical beamforming together. Nevertheless, the horizontal angle resolution is restricted by the limited physical size of the sonar platform (e.g., an underwater manned vehicle or an underwater robot). What is worse, the restricted angle resolution in the horizontal direction will degrade the imaging performance of the vertical beamforming. To solve the problem, we design a MIMO sonar array layout for the 3-D forward-looking imaging. The designed MIMO sonar array is composed of two sparsely located transmitting transducers and several parallel ULAs. By doing so, the horizontal angle resolution doubles that of a traditional sonar array (which is composed of one transmitter and several paralleled ULAs), and the vertical imaging ability is substantially improved. We give an example of the MIMO sonar array, and give the imaging performance analysis to validate the effectiveness of the designed array layout.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125021660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867333
J. Patris, D. Komatitsch, M. Sepúlveda, Macarena Santos, H. Glotin, Franck Malige, Susannah J. Buchan, M. Asch
In the context of passive acoustic monitoring of large whales, we propose a new method for localizing blue whales (Balaenoptera musculus) from the acoustic recordings of only one sensor. We use a precise modelling of the sound propagation thanks to SPECFEM, a spectral element code for solving wave propagation equations. Based on field measurements in Northern Chile, we ran a simulation on a large supercomputer. We also exploited a recording device, Bombyx II, for one and a half months, with visual monitoring of the zone by a group of experts. We find that the method applied to the south east Pacific song of blue whales gives theoretical results of about 50% success in position recovery. Since we have redundancy in our data, we were able to locate the whale with a precision of 500 m over a box of 10 km by 5 km in the case when we have both visual detection and a strong acoustic signal. More tests should be performed before validating this method, but these first results are encouraging.
{"title":"Mono-hydrophone localization of baleen whales: a study of propagation using a spectral element method applied in Northern Chile","authors":"J. Patris, D. Komatitsch, M. Sepúlveda, Macarena Santos, H. Glotin, Franck Malige, Susannah J. Buchan, M. Asch","doi":"10.1109/OCEANSE.2019.8867333","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867333","url":null,"abstract":"In the context of passive acoustic monitoring of large whales, we propose a new method for localizing blue whales (Balaenoptera musculus) from the acoustic recordings of only one sensor. We use a precise modelling of the sound propagation thanks to SPECFEM, a spectral element code for solving wave propagation equations. Based on field measurements in Northern Chile, we ran a simulation on a large supercomputer. We also exploited a recording device, Bombyx II, for one and a half months, with visual monitoring of the zone by a group of experts. We find that the method applied to the south east Pacific song of blue whales gives theoretical results of about 50% success in position recovery. Since we have redundancy in our data, we were able to locate the whale with a precision of 500 m over a box of 10 km by 5 km in the case when we have both visual detection and a strong acoustic signal. More tests should be performed before validating this method, but these first results are encouraging.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125545383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867525
K. Mizuno, P. Cristini, D. Komatitsch, Y. Capdeville
Acoustic systems with various operating frequencies are commonly used for the detection of objects buried in the marine sediments. However, the propagation of acoustic waves in sediments is generally much more complicated than in water because sediments are, in general, granular media composed of solid and fluid parts. It makes the understanding of signals more difficult and engineers have to rely on a cut-and-try method for the design of the new sub-bottom devices which results in an increase of the total cost of sea surveys. Therefore, a better understanding of wave propagation in the granular media and of the numerical model used for the prediction of reflected signals from buried objects is required. In the present study, we evaluate the performances of the prediction tools based on the spectral element method for the simulation of backscattered signals by comparing them to experimental results obtained in a tank filled with water and calibrated glass beads having a wide range of ratio of the grain size to the wavelength.
{"title":"Numerical and experimental study on wave propagation in granular media using a spectral-element method","authors":"K. Mizuno, P. Cristini, D. Komatitsch, Y. Capdeville","doi":"10.1109/OCEANSE.2019.8867525","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867525","url":null,"abstract":"Acoustic systems with various operating frequencies are commonly used for the detection of objects buried in the marine sediments. However, the propagation of acoustic waves in sediments is generally much more complicated than in water because sediments are, in general, granular media composed of solid and fluid parts. It makes the understanding of signals more difficult and engineers have to rely on a cut-and-try method for the design of the new sub-bottom devices which results in an increase of the total cost of sea surveys. Therefore, a better understanding of wave propagation in the granular media and of the numerical model used for the prediction of reflected signals from buried objects is required. In the present study, we evaluate the performances of the prediction tools based on the spectral element method for the simulation of backscattered signals by comparing them to experimental results obtained in a tank filled with water and calibrated glass beads having a wide range of ratio of the grain size to the wavelength.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125624939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867410
Randall Balestriero, H. Glotin
Acoustic monitoring is used to study marine mammals in oceans. Automated analysis for captured sound is almost essential because of the large quantity of data. The deep learning approach is an efficient method, however acoustic features are often not adapted. Convolutional Neural Net can be seen as an optimal kernel decomposition, nevertheless it requires large amount of training data to learn its kernels. An alternative using pre-imposed kernels and thus not requiring any amount of data is the scattering framework which imposes as kernels wavelet filters. Our research focuses on adaptive time-frequency decomposition of bioacoustic signal, based on cubic spline learning representation. We give the theoretical derivations of the model, and demonstrates efficient real applications of various signal, including chirps of songs of Blue Whale.
{"title":"Wavelet Learning by Adaptive Hermite Cubic Splines applied to Bioacoustic Chirps","authors":"Randall Balestriero, H. Glotin","doi":"10.1109/OCEANSE.2019.8867410","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867410","url":null,"abstract":"Acoustic monitoring is used to study marine mammals in oceans. Automated analysis for captured sound is almost essential because of the large quantity of data. The deep learning approach is an efficient method, however acoustic features are often not adapted. Convolutional Neural Net can be seen as an optimal kernel decomposition, nevertheless it requires large amount of training data to learn its kernels. An alternative using pre-imposed kernels and thus not requiring any amount of data is the scattering framework which imposes as kernels wavelet filters. Our research focuses on adaptive time-frequency decomposition of bioacoustic signal, based on cubic spline learning representation. We give the theoretical derivations of the model, and demonstrates efficient real applications of various signal, including chirps of songs of Blue Whale.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126152503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867555
Rania Bassila, Théo Bertet, L. Somaglino, M. Bouhier, H. Glotin, Paul Best, Valentin Baron, C. Noel, P. Cristini, Florent Fayet, B. Nicolas, J. Mars
The project’s main objective is to establish the feasibility of a new service and an associated system aimed to estimate the noise radiated into the water by the equipment and systems deployed on the ground during deep offshore sea floor operations. Based on this estimation, the main purpose of the project will be to assess the acoustic environmental impact on marine fauna. It will also make it possible to assess the acoustic disturbance generated by the devices or systems deployed on the ground on the operation of the sub-robots monitoring and implement adaptive strategies to prevent malfunctions of the latter. The project involves technological developments and system validation through deep sea experiments.
{"title":"ABYSOUND, an end to end system for noise impact measurement of deep sea mining production tools","authors":"Rania Bassila, Théo Bertet, L. Somaglino, M. Bouhier, H. Glotin, Paul Best, Valentin Baron, C. Noel, P. Cristini, Florent Fayet, B. Nicolas, J. Mars","doi":"10.1109/OCEANSE.2019.8867555","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867555","url":null,"abstract":"The project’s main objective is to establish the feasibility of a new service and an associated system aimed to estimate the noise radiated into the water by the equipment and systems deployed on the ground during deep offshore sea floor operations. Based on this estimation, the main purpose of the project will be to assess the acoustic environmental impact on marine fauna. It will also make it possible to assess the acoustic disturbance generated by the devices or systems deployed on the ground on the operation of the sub-robots monitoring and implement adaptive strategies to prevent malfunctions of the latter. The project involves technological developments and system validation through deep sea experiments.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127933837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867083
B. Xerri, B. Borloz, Maissa Chagmani
The aim of this paper is the detection of a bioacoustic signal embedded in several noises such as sea noise and other bioacoustic signals (dolphins, sperm whales). All the signals are real world signals.Only second order statistics are use through the estimated correlation matrices of the signals.This paper proposes an extension of the Constrained Stochastic Matched Filter (CSMF) based on the optimization of the Signal to Noise Ratio after linear filtering. The approach proposed is a multicriteria one, merging three different versions of the CSMF, and is named Multicriteria CSMF (MCSMF).The objective is that the results obtained are better than the other methods, or at least equal to the best among the three.The results are provided on ROC curves and the method is compared to the classical method Stochastic Matched Filter (SMF).
{"title":"The Multicriteria Constrained Stochastic Matched Filter For Underwater Bioacoustic Signals","authors":"B. Xerri, B. Borloz, Maissa Chagmani","doi":"10.1109/OCEANSE.2019.8867083","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867083","url":null,"abstract":"The aim of this paper is the detection of a bioacoustic signal embedded in several noises such as sea noise and other bioacoustic signals (dolphins, sperm whales). All the signals are real world signals.Only second order statistics are use through the estimated correlation matrices of the signals.This paper proposes an extension of the Constrained Stochastic Matched Filter (CSMF) based on the optimization of the Signal to Noise Ratio after linear filtering. The approach proposed is a multicriteria one, merging three different versions of the CSMF, and is named Multicriteria CSMF (MCSMF).The objective is that the results obtained are better than the other methods, or at least equal to the best among the three.The results are provided on ROC curves and the method is compared to the classical method Stochastic Matched Filter (SMF).","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"9 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125302203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867566
Mitchell G. Borg, Q. Xiao, Steven Allsop, A. Incecik, C. Peyrard
This study elaborates a one-way fluid-structure interaction numerical model utilised in investigating the structural mechanics concerning the rotor blades comprising a ducted high-solidity tidal turbine. Coupling hydrodynamic outcomes as structural inputs in effort of acknowledging the most applicable setup, distinct designs are investigated, solid blades and cored blades, utilising fibre-reinforced composite materials, analysed within criteria related to blade axial deformation, induced radial strains, and rotor specific mass.
{"title":"Analysing Fibre Composite Designs for High-Solidity Ducted Tidal Turbine Blades","authors":"Mitchell G. Borg, Q. Xiao, Steven Allsop, A. Incecik, C. Peyrard","doi":"10.1109/OCEANSE.2019.8867566","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867566","url":null,"abstract":"This study elaborates a one-way fluid-structure interaction numerical model utilised in investigating the structural mechanics concerning the rotor blades comprising a ducted high-solidity tidal turbine. Coupling hydrodynamic outcomes as structural inputs in effort of acknowledging the most applicable setup, distinct designs are investigated, solid blades and cored blades, utilising fibre-reinforced composite materials, analysed within criteria related to blade axial deformation, induced radial strains, and rotor specific mass.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121805472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867540
Feiyun Wu, Kunde Yang, Tian Tian, Chunlong Huang, Yunchao Zhu, F. Tong
The underwater acoustic channel (UAC) exhibits strongly time delay and Doppler (DD) spread especially when the UAC is rapidly time-varying. These dynamic factors result to a serious impact on communication performance such as Inter-Symbol Interference (ISI). Hence, estimation of complex amplitude, time delay and the Dopplers of the UAC becomes the key part in underwater acoustic communication and is hopeful for improving the performance of equalization. However, the estimation is challenged by multiple factors to be estimated in delay and Doppler dimensions. This study exploits the sparsity of the UAC and develops an estimator via using Gram-Schmidt to find orthogonal bases, which leads to the fast and orthogonal way to select the supports of the dictionaries. The support list of the dictionaries constructed by probe signal can be used for estimating the DD functions from a noisy received signal. Matching Pursuit (MP) and Least Square (LS) methods are used for comparisons. The effectiveness of the proposed method is verified by the experimental data.
{"title":"Estimation of Doubly Spread Underwater Acoustic Channel via Gram-Schmidt Matching Pursuit","authors":"Feiyun Wu, Kunde Yang, Tian Tian, Chunlong Huang, Yunchao Zhu, F. Tong","doi":"10.1109/OCEANSE.2019.8867540","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867540","url":null,"abstract":"The underwater acoustic channel (UAC) exhibits strongly time delay and Doppler (DD) spread especially when the UAC is rapidly time-varying. These dynamic factors result to a serious impact on communication performance such as Inter-Symbol Interference (ISI). Hence, estimation of complex amplitude, time delay and the Dopplers of the UAC becomes the key part in underwater acoustic communication and is hopeful for improving the performance of equalization. However, the estimation is challenged by multiple factors to be estimated in delay and Doppler dimensions. This study exploits the sparsity of the UAC and develops an estimator via using Gram-Schmidt to find orthogonal bases, which leads to the fast and orthogonal way to select the supports of the dictionaries. The support list of the dictionaries constructed by probe signal can be used for estimating the DD functions from a noisy received signal. Matching Pursuit (MP) and Least Square (LS) methods are used for comparisons. The effectiveness of the proposed method is verified by the experimental data.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116664804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data assimilation (DA) and machine learning (ML) are empirically compared for automatic daily fish catch forecasting (DFCF). ML would be a promising approach if large-scale data are available for training. Otherwise, DA would perform well, where prior knowledge on a monitoring target is incorporated into modeling. The present study aims to clarify the robustness of both approaches in DFCF with a small amount of data, and their evolution as the amount of training data increases. Experimental comparisons using catch and meteorological data demonstrate that a DA-based DFCF system yields a significant improvement over an ML-based systems with a small amount of data, and is comparable with ML-based systems with sufficient amount of data.
{"title":"Data Assimilation Versus Machine Learning: Comparative Study Of Fish Catch Forecasting","authors":"Yuka Horiuchi, Yuya Kokaki, Tetsunori Kobayashi, Tetsuji Ogawa","doi":"10.1109/OCEANSE.2019.8867066","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867066","url":null,"abstract":"Data assimilation (DA) and machine learning (ML) are empirically compared for automatic daily fish catch forecasting (DFCF). ML would be a promising approach if large-scale data are available for training. Otherwise, DA would perform well, where prior knowledge on a monitoring target is incorporated into modeling. The present study aims to clarify the robustness of both approaches in DFCF with a small amount of data, and their evolution as the amount of training data increases. Experimental comparisons using catch and meteorological data demonstrate that a DA-based DFCF system yields a significant improvement over an ML-based systems with a small amount of data, and is comparable with ML-based systems with sufficient amount of data.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128977575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-17DOI: 10.1109/OCEANSE.2019.8867057
F. Shubitidze, B. Barrowes, I. Shamatava
Electromagnetic induction (EMI) sensing phenomenon are investigated for a conducting and multilayer environment to aid in underwater unexploded ordnance (UXO) detection and classification. The marine environment introduces complexities, such salinity gradient, sharp conductivity changes at air-water-sediment etc., which adversely can affect targets EMI signals and make underwater targets classification more difficult problem than classifying the same buried targets on land. The sensitivity of a secondary EMI signal with respect the water/air and/or water/sediment boundaries and temporal (diffusive EM field propagation speed) variability of EMI fields in an underwater (UW) environment are studied and demonstrated using the unconditionally stable Crank-Nicolson finite different time domain method (FDTD).
{"title":"EMI Sensing for Underwater Metallic Targets Detection and Classification","authors":"F. Shubitidze, B. Barrowes, I. Shamatava","doi":"10.1109/OCEANSE.2019.8867057","DOIUrl":"https://doi.org/10.1109/OCEANSE.2019.8867057","url":null,"abstract":"Electromagnetic induction (EMI) sensing phenomenon are investigated for a conducting and multilayer environment to aid in underwater unexploded ordnance (UXO) detection and classification. The marine environment introduces complexities, such salinity gradient, sharp conductivity changes at air-water-sediment etc., which adversely can affect targets EMI signals and make underwater targets classification more difficult problem than classifying the same buried targets on land. The sensitivity of a secondary EMI signal with respect the water/air and/or water/sediment boundaries and temporal (diffusive EM field propagation speed) variability of EMI fields in an underwater (UW) environment are studied and demonstrated using the unconditionally stable Crank-Nicolson finite different time domain method (FDTD).","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128744902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：