MULTI-ROTOR UAV CLASSIFIER
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 model
of scattering of radar signals from UAVs is used. Recently, the demand for UAV classification has
been quite high due to a significant increase in the number of models and sales of these devices. Increasing
the computing power of processors and the development of the theory of neural networks allows you
to create new types of classifiers. When using models, it is possible to create a set of training data that is
acceptable for training a classifier neural network. The convolutional neural network of the classifier is
trained using radar images obtained using the proposed model of scattering of radar signals from
UAVs. The resulting radar images are modeled taking into account the UAV orientation angles relative
to the UAV normal coordinate system, flight speed, and various propeller parameters of the simulated
UAV. To form training data, in addition to the signal structure, white noise of a certain configuration is
added, which helps to increase the diversity of training samples to improve the learning ability of the
convolutional neural network. The use of data obtained using the model for training a neural network is
due 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 configurations
of unmanned aerial vehicles: tricopter (three propellers), quadcopter (four propellers), hexacopter
(six propellers), or octocopter (eight propellers). which complicates the use of experimental data to
create classifiers of this type.
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