IMPLEMENTATION OF AN ONBOARD ALGORITHM FOR TARGET SEARCH, IDENTIFICATION, RECOGNITION, AND SUBSEQUENT ENGAGEMENT USING TRAINED NEURAL NETWORKS

The purpose of the work is to analyze the didactic features of modern military education using advanced digital technologies.Research results: as a result, the following set of didactic functions of an intellectual learning system can be distinguished: adaptive, transformative, coordinating, forming an integral system, developing, integrating, ensuring the consolidation of knowledge, self-control, individualized approach and differentiation, selfeducation, methodological, and analytical.The development of didactics of military engineering disciplines in the digital age will be characterized by the following trends: expansion of the conceptual and terminological stock, the emergence of new theoretical concepts, didactic systems and teaching models; preservation of the main scientific functions of didactics: descriptive, explanatory and predictive; clarification of existing or discovery of new laws and patterns of the educational process, which will become the basis for the creation of normative models.The theory will be based on the phenomenological material collected during the study of distance education.Scientific novelty: the introduction of end-to-end digital technologies and innovative techniques significantly improves the quality of education and training of military specialists, as well as enhances the effectiveness of management of military structures and combat units.This approach will make it possible to adapt to changes in technology and the requirements for military training, as well as to anticipate their impact on military education in the coming years.

Authors

Publication Details

Journal
Telecommunications and Comunications
Published
2025-09-18
DOI
https://doi.org/10.21681/3034-4050-2025-4-33-42
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

IMPLEMENTATION OF AN ONBOARD ALGORITHM FOR TARGET SEARCH, IDENTIFICATION, RECOGNITION, AND SUBSEQUENT ENGAGEMENT USING TRAINED NEURAL NETWORKS

Nikita Vasiliev, Dmitry Sitdikov
Telecommunications and Comunications
Neural Networks and Applications
article

IMPLEMENTATION OF AN ONBOARD ALGORITHM FOR TARGET SEARCH, IDENTIFICATION, RECOGNITION, AND SUBSEQUENT ENGAGEMENT USING TRAINED NEURAL NETWORKS

Nikita Vasiliev, Dmitry Sitdikov
article en

Abstract

The purpose of the work is to analyze the didactic features of modern military education using advanced digital technologies.Research results: as a result, the following set of didactic functions of an intellectual learning system can be distinguished: adaptive, transformative, coordinating, forming an integral system, developing, integrating, ensuring the consolidation of knowledge, self-control, individualized approach and differentiation, selfeducation, methodological, and analytical.The development of didactics of military engineering disciplines in the digital age will be characterized by the following trends: expansion of the conceptual and terminological stock, the emergence of new theoretical concepts, didactic systems and teaching models; preservation of the main scientific functions of didactics: descriptive, explanatory and predictive; clarification of existing or discovery of new laws and patterns of the educational process, which will become the basis for the creation of normative models.The theory will be based on the phenomenological material collected during the study of distance education.Scientific novelty: the introduction of end-to-end digital technologies and innovative techniques significantly improves the quality of education and training of military specialists, as well as enhances the effectiveness of management of military structures and combat units.This approach will make it possible to adapt to changes in technology and the requirements for military training, as well as to anticipate their impact on military education in the coming years.

Telecommunications and Comunications(4)
Openalex Percentile: Top 87%
Neural Networks and Applications
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