This article is devoted to the problems of radar sensing. Herein, we have considered the tasks of modeling and recognizing radar images. The modeling technology was based on the independent creation of terrain models and objects, which were then integrated into a three-dimensional (3D) scene. This approach enabled the operative creation of a number of image variants of different classes. Recognition methods and algorithms were based on the use of the so-called conjugacy index as a measure of proximity. At the same time, support subspaces of the minimum dimension were formed by vectors, components of which were samples of the radar image. Problems of higher accuracy of recognition due to a division of classes into subclasses and a combination of the support subspace method with the neural convolutional networks were considered.
The article aimed to provide a sort of new education process including virtual reality based application. At present, in accordance with the established ways of archaeological research, archaeologists are forced to transfer the found samples for long-term storage. In such notation, there is a challenging issue to create a virtual museum with deepening experience user interaction. The modern approaches of the virtual reality were implemented by applying technologies such as the Unreal Engine (UE) and Leap Motion (LM). In the paper, we give the scheme of the implemented development workflow. The ability of interaction with objects using the interface and hand gestures on LM on UE was given.
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