MODEL OF SCATTERING OF RADAR SIGNALS FROM UAV

Abstract

In this article, a model of scattering of radar signals from unmanned aerial vehicles (UAVs) of a multi-rotor type is considered for the formation of training data for a neural network classifier. Recently, there has been an increased interest in studying the issue of detecting and classifying small unmanned aerial vehicles (UAVs), which is associated with the development of the UAV range in sales and production. In addition to the development of UAVs, an increase in the performance of computers made it possible to create classifiers using new neural network algorithms. This model generates radar images obtained as a result of the reflection of a chirp radar signal from an unmanned aerial vehicle, taking into account the configuration, characteristics, current location and flight parameters of the observed object. When calculating the reflected signal, the angles of rotation of the UAV (pitch, roll and yaw), flight speed, size and location of propellers in the current UAV configuration are taken into account. The resulting model can be useful for the formation of a training set of a classifier of unmanned aerial vehicles of a multi-rotor type, builtusing convolutional neural networks. The need to use a model that generates data for a neural network is due to the requirement for a large number of training and verification samples, as well as a wide variety of configurations of unmanned aerial vehicles, which greatly increases the complexity and cost of creating a training dataset using experimental measurements. In addition to training the neural network itself, this model can be used to assess the detection and classification of various types of multi-rotor UAVs, in the development of a specialized radar station for detecting this type of objects.