RUDN Journal of Engineering ResearchRUDN Journal of Engineering ResearchВестник Российского университета дружбы народов. Серия: Инженерные исследования2312-81432312-8151Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)3737010.22363/2312-8143-2023-24-4-305-322HMUNNVArticlesСтатьиReview ArticleArtificial intelligencedriven optimization of MEMS navigation sensors for enhanced user experienceОптимизация навигационных датчиков МЭМС с применением искусственного интеллекта для улучшения пользовательского опытаhttps://orcid.org/0009-0006-0673-1893AlizadehAliАлизадехАли

M.S Student of the Department of Mechanics and Control Processes, Academy of Engineering, RUDN Univ; M.S Student of the Department of Space Engineering, Faculty of Aerospace Engineering, K.N. Toosi University of Technology

магистрант департамента механики и процессов управления, инженерная академия, Российский университет дружбы народов; магистрант кафедры космической инженерии, факультет аэрокосмической техники, Технологический университет К.Н. Туси

ali.rim.alizadeh@gmail.comhttps://orcid.org/0000-0002-3880-6662SaltykovaOlga A.СалтыковаОльга Александровна

Ph.D. of Physico-Mathematical Sciences, Associate Professor of the Department of Mechanics and Control Processes, Academy of Engineering

кандидат физико-математических наук, доцент департамента механики и процессов управления, инженерная академия

saltykova-oa@rudn.ruhttps://orcid.org/0000-0002-8123-6968NovinzadehA. BНовинзадехАлиреза Басохбат

Ph.D. of Space Engineering, Associate Professor, Head of the Department of Space Engineering, Faculty of Aerospace Engineering

доктор наук в области космической инженерии, доцент, заведующий кафедрой космической инженерии, факультет аэрокосмической инженерии

novinzadeh@kntu.ac.irRUDN UniversityТехнологический университет имени К.Н. ТусиK.N. Toosi University of TechnologyРоссийский университет дружбы народовK.N. Toosi University of Technology31122023244VOL 24, NO4 (2023)ТОМ 24, №4 (2023)30532209012024Copyright ©; 2023, Alizadeh A., Saltykova O.A., Novinzadeh A.B.Copyright ©; 2023, Ализадех А., Салтыкова О.А., Новинзадех А.Б.2023Alizadeh A., Saltykova O.A., Novinzadeh A.B.Ализадех А., Салтыкова О.А., Новинзадех А.Б.https://creativecommons.org/licenses/by-nc/4.0/legalcodehttps://journals.rudn.ru/engineering-researches/article/view/37370

This review delves into the key area of artificial intelligence (AI)-driven optimization applied to Microelectromechanical Systems (MEMS) navigation sensors, with the primary objective of enhancing the user experience. Employing a comprehensive research methodology, it extensively explores AI-powered techniques, encompassing sensor fusion, adaptive filtering, calibration, compensation, predictive modeling, and energy efficiency. Through rigorous case studies and empirical evidence, this research provides substantial achievements, including enhanced accuracy, reduced power consumption, heightened reliability, and amplified user satisfaction, across diverse applications such as autonomous vehicles, indoor localization, wearable devices, and unmanned systems. In conclusion, this review highlights the transformative potential of AI-driven optimization in MEMS navigation sensors while acknowledging persistent challenges in computational complexity, data availability, and real-time processing. It advocates for future research focusing on innovative AI methodologies, integration with emerging technologies, adherence to human-centric design principles, and the establishment of rigorous evaluation standards. Such research promises to unlock the full potential of AI-driven optimization, ushering in advanced and user-centric navigation systems, and ultimately improving user experience across diverse areas.

Кусственного интеллекта (ИИ), применяемого к навигационным датчикам микроэлектромеханических систем (МЭМС). Основная цель - улучшение пользовательского опыта. Используя комплексный подход, исследуются методы, основанные на искусственном интеллекте, включающие слияние датчиков, адаптивную фильтрацию, калибровку, компенсацию, прогнозное моделирование и энергоэффективность. Через строгое проведение кейс-исследований и использование эмпирических данных данное исследование подтверждает значительные достижения, включая повышенную точность, снижение энергопотребления, увеличение надежности и усиление удовлетворенности пользователя, в различных приложениях, таких как автономные транспортные средства, внутреннее определение положения, носимые устройства и беспилотные системы. В заключении данное исследование подчеркивает трансформационный потенциал оптимизации на основе ИИ в навигационных датчиках МЭМС, признавая при этом наличие постоянных вызовов, таких как вычислительная сложность, доступность данных и обработка в реальном времени проведения дальнейших исследований, ориентированных на инновационные методологии ИИ, их интеграцию с передовыми технологиями с условием соблюдения принципов дизайна, ориентированных на человека, и установление строгих стандартов оценки. Подобные исследования позволят использовать весь потенциал механизмов оптимизации на основе методов ИИ, внедряя передовые и ориентированные на пользователя навигационные системы и в конечном итоге повышая уровень удобства пользователей в различных областях применения подобных систем.

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