Публікація:
Artificial neural networks for hybrid inertial-satellite navigation systems: challenges, tasks and recent research

dc.contributor.authorLashchenko, O. L.
dc.date.accessioned2025-10-09T07:43:57Z
dc.date.issued2025
dc.description.abstractThis paper addresses the problem of unreliable or falsified GNSS signals and presents advanced methodologies for their detection. The focus has been made on the integration of machine learning techniques, particularly Artificial Neural Networks (ANNs) into hybrid navigation systems to model temporal and spatial anomalies in GNSS data, enabling the system to distinguish between authentic and corrupted signals.
dc.identifier.citationLashchenko O. L. Artificial neural networks for hybrid inertial-satellite navigation systems: challenges, tasks and recent research [Electronic resours] / O. L. Lashchenko ; thesis supervisor V. B. Uspenskyi // An Innovative Model of Research Projects Aimed at the Integration of Ukraine into the European Scientific Space : book of abstr. an Annual Intern. PhD Conf., April 24, 2025 / National Technical University "Kharkiv Polytechnic Institute". – Electronic text data. – Kharkiv : NTU "KhPI", 2025. – P. 146-147.
dc.identifier.urihttps://repository.kpi.kharkov.ua/handle/KhPI-Press/93844
dc.language.isoen
dc.publisherNational Technical University "Kharkiv Polytechnic Institute"
dc.subjectGNSS
dc.subjectinertial navigation
dc.subjectartificial neural networks
dc.titleArtificial neural networks for hybrid inertial-satellite navigation systems: challenges, tasks and recent research
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublicationc03ebf2f-4c62-4ffb-82eb-8a2151d24d4e
relation.isAuthorOfPublication.latestForDiscoveryc03ebf2f-4c62-4ffb-82eb-8a2151d24d4e

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