References

gyroscopy

Гироскопия и навигация

Giroskopiya i Navigatsiya / Gyroscopy and Navigation

0869-70352075-0927

AO «Концерн «ЦНИИ «Электроприбор»

XMYLOI

gyroscopy-86

Research Article

Статьи

Обзор методов инерциальной навигации на основе глубокого обучения для навигации АНПА на больших дистанциях

Deep Learning-Based Inertial Navigation Technology for Autonomous Underwater Vehicle Long-Distance Navigation – A Review

Хэ

Цинь Юань

Qin Yuan

Цинь Юань Хэ, доктор наук

Пекин

Qin Yuan He

Beijing

Хуа Пэн

Hua Peng

Хуа Пэн Ю, доктор наук

Пекин

Hua Peng Yu

Beijing

Фан

Ю Чэнь

Fang

Yu Chen

Ю Чэнь Фан, доктор наук

факультет автоматизированного проектирования

Чэнду

Yu Chen Fang

Faculty of Computer-aided Design

Chengdu

Национальный институт инновационных оборонных технологий, Ака- Доктор наук, Национальный институт инновационных оборонных технологий, Академия военных наукКитайNational Innovation Institute of Defense Technology Academy of Military ScienceChina

Национальный институт инновационных оборонных технологий, Академия военных наукКитайNational Innovation Institute of Defense Technology Academy of Military ScienceChina

Университет электронных наук и технологий КитаяКитайSchool of Automation Engineering University of Electronic Science and Technology of ChinaChina

2023

19052025

313122135

2025

Хэ Ц.Ю., Ю Х.П., Фан Ю.Ч.

He Q.Y., Yu H.P., Fang Y.C.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://www.gyroscopy.ru/jour/article/view/86

Инерциальные методы навигации автономных необитаемых подводных аппаратов (АНПА) позволяют обеспечить выполнение ими сложных задач в автоматическом режиме. Разработки в области традиционной инерциальной навигации ведутся в течение многих лет, и существует потребность в новых технических решениях. С помощью метода глубокого обучения можно автоматически выбирать и извлекать ключевые признаки в обрабатываемых данных, что широко применяется для распознавания изображений, речи, обработки текстов и в других областях. Хорошие результаты достигаются при обработке последовательно поступающих данных, например текста и речи. Очевидно, что выходные данные инерциальной навигации относятся к такому же типу информации. Многочисленные исследования показали, что модели на основе глубоких нейронных сетей можно использовать для снижения уровня шума инерциальных датчиков и дрейфа средств инерциальной навигации, а также комплексирования инерциальных данных с данными других датчиков. Кроме того, эти модели позволяют прогнозировать и уменьшать погрешности инерциальной навигации при длительном подводном плавании. В статье приводится обзор методов инерциальной навигации АНПА на основе глубокого обучения, включая новейшие достижения и тенденции развития.

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

инерциальная навигацияподводное позиционированиеглубокое обучениеАНПА

Inertial NavigationUnderwater LocalizationDeep LearningAUV

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