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Localization of underwater gliders with acoustic travel-time in an observation network
Sun J(孙洁); Liu SJ(刘世杰); Yu JC(俞建成); Zhang AQ(张艾群); Zhang FM(张福民)
Department水下机器人研究室
Conference NameOCEANS 2016 - Shanghai
Conference DateApril 10-13, 2016
Conference PlaceShanghai, China
Source PublicationOCEANS 2016 - Shanghai
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2016
Pages1-5
Indexed ByEI ; CPCI(ISTP)
EI Accession number20162902613042
WOS IDWOS:000386521800168
Contribution Rank1
ISSN0197-7385
ISBN978-1-4673-9724-7
AbstractThis paper presents a method to localize underwater gliders in observation networks, and this method is also applicable to other vehicles. Utilizing the travel time of acoustic signals from near-surface acoustic sources, the dead-reckoning positions of underwater gliders can be refined. Specifically, compare two kinds of travel time, one is simulated by an acoustic model for a series of possible positions of underwater gliders, and the other is the measured travel time for real positions of underwater gliders, then the most possible positions of gliders can be estimated, also the speed of ocean currents. The method formulation is based on particle filters, which could improve performance of the positioning system over time. Simulation is conducted according to experimental environment in the South China Sea, and the positioning results are compared with the dead-reckoning positions derived from experimental data, validating the effectiveness of this method.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/18771
Collection水下机器人研究室
Corresponding AuthorSun J(孙洁)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
3.School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, United States
Recommended Citation
GB/T 7714
Sun J,Liu SJ,Yu JC,et al. Localization of underwater gliders with acoustic travel-time in an observation network[C]. Piscataway, NJ, USA:IEEE,2016:1-5.
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