research-article

Underwater acoustic intensity field reconstruction by kriged compressive sensing

Authors:

Fumin ZhangAuthors Info & Claims

WUWNet '18: Proceedings of the 13th International Conference on Underwater Networks & Systems

Article No.: 5, Pages 1 - 8

https://doi.org/10.1145/3291940.3291971

Published: 03 December 2018 Publication History

Abstract

This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea.

References

[1]

2018. Accessed from: Acoustics Toolbox. http://oalib.hlsresearch.com/Modes/AcousticsToolbox/index.html.

[2]

2018. Data from: Geospatial Data and Services. https://maps.ngdc.noaa.gov/index.html.

[3]

2018. Data from: HYCOM+NCODA Gulf of Mexico 1/25° analysis. https://hycom.org/data/goml0pt04/expt-32pt5.

[4]

Thomas Blumensath and Mike E Davies. 2008. Iterative thresholding for sparse approximations. Journal of Fourier analysis and Applications 14, 5--6 (2008), 629--654.

[5]

Leonid Maksimovich Brekhovskikh, Yu P Lysanov, and Robert T Beyer. 1991. Fundamentals of ocean acoustics. The Journal of the Acoustical Society of America 90, 6 (1991), 3382--3383.

[6]

E. J. Candes, J. K. Romberg, and T. Tao. 2006. Stable Signal Recovery from Incomplete and Inaccurate Measurements. Communications on Pure and Applied Mathematics LIX (2006), 1207--1223.

[7]

Sumit Datta and Bhabesh Deka. 2018. Magnetic resonance image reconstruction using fast interpolated compressed sensing. Journal of Optics 47, 2 (2018), 154--165.

[8]

Minh N. Do and Martin Vetterli. 2005. The contourlet transform: An efficient directional multiresolution image representation. IEEE Transactions on Image Processing 14, 12 (2005), 2091--2106.

Digital Library

[9]

Paul C Etter. 2013. Underwater acoustic modeling and simulation. CRC Press.

[10]

Douglas Horner. 2013. A data-driven framework for rapid modeling of wireless communication channels. Ph.D. Dissertation. Monterey, California: Naval Postgraduate School.

[11]

Bruce M. Howe. 2006. Acoustic seaglider. The Journal of the Acoustical Society of America 120, 5 (2006), 3048.

[12]

Quan Huynh-Thu and Mohammed Ghanbari. 2008. Scope of validity of PSNR in image/video quality assessment. Electronics letters 44, 13 (2008), 800--801.

[13]

Chengbo Li, Charles C Mosher, and Sam T Kaplan. 2012. Interpolated compressive sensing for seismic data reconstruction. In SEG Technical Program Expanded Abstracts 2012. Society of Exploration Geophysicists, 1--6.

[14]

Sungkwang Mun and James E Fowler. 2009. Block compressed sensing of images using directional transforms. In Image Processing (ICIP), 2009 16th IEEE International Conference on. IEEE, 3021--3024.

Digital Library

[15]

Mark New, Mike Hulme, and Phil Jones. 2000. Representing twentieth-century space-time climate variability. Part II: Development of 1901--96 monthly grids of terrestrial surface climate. Journal of climate 13, 13 (2000), 2217--2238.

[16]

Justin Romberg. 2008. Imaging via compressive sampling {introduction to compressive sampling and recovery via convex programming}. IEEE Signal Processing Magazine 25, 2 (2008), 14--20.

[17]

Michael L Stein. 2012. Interpolation of spatial data: some theory for kriging. Springer Science & Business Media.

[18]

Jie Sun, Aijun Song, Jiancheng Yu, Aiqun Zhang, and Fumin Zhang. 2017. Underwater acoustic field reconstruction using a compressive sensing approach. In OCEANS-Anchorage, 2017. IEEE, 1--5.

[19]

Joel A Tropp and Stephen J Wright. 2010. Computational methods for sparse solution of linear inverse problems. Proc. IEEE 98, 6 (2010), 948--958.

[20]

Michael B Wakin, Jason N Laska, Marco F Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin F Kelly, and Richard G Baraniuk. 2006. An architecture for compressive imaging. In Image Processing, 2006 IEEE International Conference on. IEEE, 1273--1276.

[21]

Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13, 4 (2004), 600--612.

Digital Library

[22]

Wencen Wu, Aijun Song, Paul Varnell, and Fumin Zhang. 2014. Cooperatively mapping of the underwater acoustic channel by robot swarms. In Proceedings of the International Conference on Underwater Networks & Systems. ACM, 20.

Digital Library

[23]

Jiancheng Yu, Fumin Zhang, Aiqun Zhang, Wenming Jin, and Yu Tian. 2013. Motion parameter optimization and sensor scheduling for the sea-wing underwater glider. IEEE Journal of Oceanic Engineering 38, 2 (2013), 243--254.

Cited By

Sun JLiu SZhang FSong AYu JZhang A(2021)A Kriged Compressive Sensing Approach to Reconstruct Acoustic Fields From Measurements Collected by Underwater VehiclesIEEE Journal of Oceanic Engineering10.1109/JOE.2020.297427046:1(294-306)Online publication date: Jan-2021
https://doi.org/10.1109/JOE.2020.2974270
Sun JHu FJin WWang JWang XLuo YYu JZhang A(2020)Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time DifferencesSensors10.3390/s2003089320:3(893)Online publication date: 7-Feb-2020
https://doi.org/10.3390/s20030893
Kontoudis GStilwell D(2019)A Comparison of Kriging and Cokriging for Estimation of Underwater Acoustic Communication PerformanceProceedings of the 14th International Conference on Underwater Networks & Systems10.1145/3366486.3366515(1-8)Online publication date: 23-Oct-2019
https://dl.acm.org/doi/10.1145/3366486.3366515

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cover image ACM Other conferences

WUWNet '18: Proceedings of the 13th International Conference on Underwater Networks & Systems

December 2018

261 pages

ISBN:9781450361934

DOI:10.1145/3291940

General Chairs:
Jingdong Chen
Northwestern Polytechnical University
,
Jun Liu
Jilin University

Copyright © 2018 ACM.

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Publication History

Published: 03 December 2018

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National Natural Science Foundation of China
State Key Laboratory of Robotics at Shenyang Institute of Automation

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WUWNet'18

WUWNet'18: The 13th ACM International Conference on Underwater Networks & Systems

December 3 - 5, 2018

Shenzhen, China

Acceptance Rates

WUWNet '18 Paper Acceptance Rate 11 of 23 submissions, 48%;

Overall Acceptance Rate 84 of 180 submissions, 47%

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Cited By

Sun JLiu SZhang FSong AYu JZhang A(2021)A Kriged Compressive Sensing Approach to Reconstruct Acoustic Fields From Measurements Collected by Underwater VehiclesIEEE Journal of Oceanic Engineering10.1109/JOE.2020.297427046:1(294-306)Online publication date: Jan-2021
https://doi.org/10.1109/JOE.2020.2974270
Sun JHu FJin WWang JWang XLuo YYu JZhang A(2020)Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time DifferencesSensors10.3390/s2003089320:3(893)Online publication date: 7-Feb-2020
https://doi.org/10.3390/s20030893
Kontoudis GStilwell D(2019)A Comparison of Kriging and Cokriging for Estimation of Underwater Acoustic Communication PerformanceProceedings of the 14th International Conference on Underwater Networks & Systems10.1145/3366486.3366515(1-8)Online publication date: 23-Oct-2019
https://dl.acm.org/doi/10.1145/3366486.3366515

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