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AUV dead-reckoning navigation based on neural network using a single accelerometer
Xie, Yanxin; Liu J(刘军); Hu CQ(胡成全); Cui JH(崔军红); Xu HL(徐红丽)
Department海洋信息技术装备中心
Conference Name11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016
Conference DateOctober 24-26, 2016
Conference PlaceShanghai, China
Author of SourceACM Special Interest Group on Mobility of Systems, Users, Data, and Computing (SIGMOBILE); Acoustical Society of China (ASC); Association for Computing Machinery (ACM)
Source PublicationWUWNet 2016 - 11th ACM International Conference on Underwater Networks and Systems
PublisherACM
Publication PlaceNew York
2016
Pages1-5
Indexed ByEI
EI Accession number20165003114036
Contribution Rank1
ISBN978-1-4503-4637-5
KeywordDead-reckoning Neural Networks Accelerometer
AbstractThe accuracy of the Autonomous Underwater Vehicles (AUVs) navigation system determines whether they can safely operate and return. Traditional Dead-reckoning (DR) relies on the inertial sensors such as gyroscope and accelerometer. A major challenge for DR navigation is from measurement error of the inertial sensors (gyroscope, accelerometer, etc.), especially when the AUV is near or at the ocean surface. The AUV's motion is affected by ocean waves, and its pitch angle changes rapidly with the waves. This rapid change and the measurement errors will cause great noise to the direction measured by gyroscopes, and then lead to a large error to the DR navigation. To address this problem, a novel DR method based on neural network (DR-N) is proposed to explore the time-varying relationship between acceleration measurement and orientation measurement, which leverages acoustic localization and neural network estimate timely pitch angle through the explored time-varying relationship. This method enables AUV's DR navigation with a single acceleration, without relying on both acceleration and gyroscope. Most importantly, we can improve the accuracy of AUV navigation through avoiding DR errors caused by gyroscope noise at the sea surface. Simulations show DR-N significantly improves navigation accuracy.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/20082
Collection海洋信息技术装备中心
Corresponding AuthorLiu J(刘军)
Affiliation1.State Key Laboratory of Robotics, CAS No. 114 Nanta Street, Shenhe District, Shenyang, China
2.College of Computer Science and Technology, Jilin University, Changchun, China
3.Underwater Sensor Network Lab., University of Connecticut, Storrs, CT 06269, United States
4.Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
Recommended Citation
GB/T 7714
Xie, Yanxin,Liu J,Hu CQ,et al. AUV dead-reckoning navigation based on neural network using a single accelerometer[C]//ACM Special Interest Group on Mobility of Systems, Users, Data, and Computing (SIGMOBILE); Acoustical Society of China (ASC); Association for Computing Machinery (ACM). New York:ACM,2016:1-5.
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