Deep Learning-Based Inertial Navigation Technology for Autonomous Underwater Vehicle Long-Distance Navigation – A Review
EDN: XMYLOI
Abstract
Autonomous navigation technology is the key technology for Autonomous Underwater Vehicle (AUV) to achieve automated, intelligent operation and task processing. Inertial navigation technology is the core of autonomous navigation technology for AUV. Traditional inertial navigation technology has been developed for many years, and it is necessary to find new breakthroughs. Deep learning can automatically select and extract key features of input data, which has been widely used in image recognition, speech recognition, natural language processing and other fields, and has good results in processing sequential data such as text and speech. Inertial navigation data clearly belongs to this type of data, and many scholars in the industry have conducted related research and design, and found that deep neural network models can be used to calibrate the noise of inertial sensors, reduce the drift of inertial navigation mechanisms, and fuse inertial information with other sensor information, with good effects in solving the prediction and error suppression of inertial navigation during long-term underwater voyages. This article provides a comprehensive review of deep learning-based inertial navigation for AUV, including the latest research progress and development trend direction
About the Authors
Qin Yuan He
National Innovation Institute of Defense Technology Academy of Military Science
China
China
Qin Yuan He
Beijing
Hua Peng Yu
National Innovation Institute of Defense Technology Academy of Military Science
China
China
Hua Peng Yu
Beijing
Yu Chen Fang
School of Automation Engineering University of Electronic Science and Technology of China
China
China
Yu Chen Fang
Faculty of Computer-aided Design
Chengdu
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Review
For citations:
He Q.Yu., Yu H.P., Fang Yu.Ch. Deep Learning-Based Inertial Navigation Technology for Autonomous Underwater Vehicle Long-Distance Navigation – A Review. Gyroscopy and Navigation. 2023;31(3):122-135. (In Russ.) EDN: XMYLOI
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