Underwater Acoustic Intensity Field Reconstruction by Kriged Compressive Sensing
文献类型:会议论文
作者 | Sun J(孙洁)1,2![]() ![]() ![]() |
出版日期 | 2018 |
会议日期 | December 3-5, 2018 |
会议地点 | Shenzhen, China |
关键词 | compressive sensing kriging underwater acoustic sensing underwater gliders |
页码 | 1-8 |
英文摘要 | 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. |
源文献作者 | CSSC Systems Engineering Research Institute ; Institute of Acoustics, Chinese Academy of Sciences ; Jilin University ; Northwestern Polytechnical University ; Shenzhen University |
产权排序 | 1 |
会议录 | Proceedings of the 13th ACM International Conference on Underwater Networks and Systems, WUWNet 2018
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会议录出版者 | ACM |
会议录出版地 | New York |
语种 | 英语 |
ISBN号 | 978-1-4503-6193-4 |
源URL | [http://ir.sia.cn/handle/173321/23778] ![]() |
专题 | 海洋机器人卓越创新中心 |
通讯作者 | Sun J(孙洁) |
作者单位 | 1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of SciencesShenyang,110016, P.R.China 2.University of Chinese Academy of Sciences, Beijing, 100049, P.R.China 3.Department of Electrical Engineering, University of Alabama, Tuscaloosa, Alabama 35487, USA 4.School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA |
推荐引用方式 GB/T 7714 | Sun J,Yu JC,Zhang AQ,et al. Underwater Acoustic Intensity Field Reconstruction by Kriged Compressive Sensing[C]. 见:. Shenzhen, China. December 3-5, 2018. |
入库方式: OAI收割
来源:沈阳自动化研究所
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