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Underwater Acoustic Field Reconstruction Using a Compressive Sensing Approach
Sun J(孙洁); Song AJ(宋爱军); Yu JC(俞建成); Zhang AQ(张艾群); Zhang FM(张福民)
Conference NameOCEANS 2017 MTS/IEEE Anchorage
Conference DateSeptember 18-21, 2017
Conference PlaceAnchorage, USA
Source PublicationOCEANS 2017 MTS/IEEE Anchorage
Publication PlaceNew York
Indexed ByEI ; CPCI(ISTP)
EI Accession number20182405299314
WOS IDWOS:000455012000322
Contribution Rank1

In this paper, we apply a block-based compressive sensing (BCS) architecture to address the reconstruction of underwater acoustic intensity fields. Although with anisotropic characteristics, underwater acoustic intensity fields can be compressed or represented in sparse transform domains. The distinct advantages of the BCS method include no need for any prior knowledge of the interested acoustic fields and no need for complete sampling coverage. Both benefits can facilitate experimentation and improve reconstruction precision. We demonstrated the recovery capability by applying this algorithm to the reconstruction of simulated acoustic fields in the Gulf of Mexico, where the bathymetry and water column showed high levels of spatial variability. Further, a field experiment was conducted at a local river, Lake Tamaha, where an autonomous underwater vehicle navigated during acoustic transmissions. Reconstruction performance comparisons were made between the BCS and interpolation methods at different measurement ratios.

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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Corresponding AuthorSun J(孙洁)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang,110016, P.R.China
2.University of Chinese Academy of Sciences, Beijing, 100049, P.R.China
3.School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
4.Department of Electrical Engineering, University of Alabama, Tuscaloosa, Alabama 35487, USA
Recommended Citation
GB/T 7714
Sun J,Song AJ,Yu JC,et al. Underwater Acoustic Field Reconstruction Using a Compressive Sensing Approach[C]. New York:IEEE,2017:1-5.
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