КЛАССИФИКАТОР БПЛА МУЛЬТИРОТОРНОГО ТИПА

Abstract

This article discusses a classifier of radar signals reflected from unmanned aerial vehicles(UAVs), based on neural networks. In the proposed classifier, for the formation of training data, a modelof scattering of radar signals from UAVs is used. Recently, the demand for UAV classification hasbeen quite high due to a significant increase in the number of models and sales of these devices. Increasingthe computing power of processors and the development of the theory of neural networks allows youto create new types of classifiers. When using models, it is possible to create a set of training data that isacceptable for training a classifier neural network. The convolutional neural network of the classifier istrained using radar images obtained using the proposed model of scattering of radar signals fromUAVs. The resulting radar images are modeled taking into account the UAV orientation angles relativeto the UAV normal coordinate system, flight speed, and various propeller parameters of the simulatedUAV. To form training data, in addition to the signal structure, white noise of a certain configuration isadded, which helps to increase the diversity of training samples to improve the learning ability of theconvolutional neural network. The use of data obtained using the model for training a neural network isdue to the need to use a large number of training samples with various UAV movement parameters,such as height, speed, direction, orientation in space, as well as a wide variety of possible configurationsof unmanned aerial vehicles: tricopter (three propellers), quadcopter (four propellers), hexacopter(six propellers), or octocopter (eight propellers). which complicates the use of experimental data tocreate classifiers of this type.