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A Kriged Compressive Sensing Approach to Reconstruct Acoustic Fields From Measurements Collected by Underwater Vehicles
Sun J(孙洁)1,2,3; Liu SJ(刘世杰)1,2,3; Zhang FM(张福民)4; Song AJ(宋爱军)5; Yu JC(俞建成)1,2; Zhang AQ(张艾群)1,2
Department海洋机器人前沿技术中心
Source PublicationIEEE Journal of Oceanic Engineering
ISSN0364-9059
2021
Volume46Issue:1Pages:294-306
Indexed BySCI ; EI
EI Accession number20201308343965
WOS IDWOS:000607812000020
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China under Grant 61673370 and Grant U1709202 ; National Key Research and Development Project under Grant 2016YFC0301201 ; State Key Laboratory of Robotics at Shenyang Institute of Automation under Grant 2020-Z06 and Grant 2014-Z02 ; U.S. National Science Foundation under Grants CNS-1828678 and S&AS-1849228
KeywordAcoustic field reconstruction compressive sensing (CS) kriging interpolation underwater mobile platforms
Abstract

This article presents a kriged compressive sensing (KCS) approach to reconstruct acoustic fields using measurements collected by underwater mobile sensing platforms. The KCS approach has two steps. First, initial estimates are obtained from a kriging method by leveraging spatial statistical correlation properties of the acoustic fields. Second, selected initial estimates, treated as virtual samples, are combined with the measurements to perform field reconstruction through compressive sensing. To differentiate the fidelity between real measurements and virtual samples, we use the kriging variance to determine weight coefficients for the virtual samples estimated from kriging. Simulation results show that the proposed KCS approach can improve the reconstruction performance, in terms of the peak signal-to-noise ratio and structural similarity metrics. The KCS performance has been validated based on the acoustic intensity measurements collected by an autonomous underwater vehicle in a lake. The KCS methods have also been applied to process the ambient sound level measurements collected by an underwater glider in the South China Sea. The proposed KCS method leads to better performance than either the compressive sensing or the kriging method alone.

Language英语
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26642
Collection海洋机器人前沿技术中心
Corresponding AuthorSong AJ(宋爱军)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016 China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169 China
3.University of Chinese Academy of Sciences, Beijing 100049 China
4.School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
5.Department of Electrical Engineering, University of Alabama, Tuscaloosa, AL 35487 USA
Recommended Citation
GB/T 7714
Sun J,Liu SJ,Zhang FM,et al. A Kriged Compressive Sensing Approach to Reconstruct Acoustic Fields From Measurements Collected by Underwater Vehicles[J]. IEEE Journal of Oceanic Engineering,2021,46(1):294-306.
APA Sun J,Liu SJ,Zhang FM,Song AJ,Yu JC,&Zhang AQ.(2021).A Kriged Compressive Sensing Approach to Reconstruct Acoustic Fields From Measurements Collected by Underwater Vehicles.IEEE Journal of Oceanic Engineering,46(1),294-306.
MLA Sun J,et al."A Kriged Compressive Sensing Approach to Reconstruct Acoustic Fields From Measurements Collected by Underwater Vehicles".IEEE Journal of Oceanic Engineering 46.1(2021):294-306.
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