title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¼Ð¸ÐºÑÐ¾Ð´Ð¾Ð¿Ð»ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ ÑÐ¸Ð³Ð½Ð°ÑÑÑ ÑÐ°Ð´Ð°ÑÐ¾Ð² Ð½ÐµÐ¿ÑÐµÑÑÐ²Ð½Ð¾Ð³Ð¾ Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Application of convolutional neural networks for continuous wave radar micro-doppler signatures processing
Creators: name: ÐÐ°ÑÐ´Ð¸ÐµÐ², ÐÑÑÐµÐ¼
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÑÐ°Ð´Ð°Ñ Ð¼Ð¸Ð»Ð»Ð¸Ð¼ÐµÑÑÐ¾Ð²Ð¾Ð³Ð¾ Ð´Ð¸Ð°Ð¿Ð°Ð·Ð¾Ð½Ð°
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð»Ð¸Ð½ÐµÐ¹Ð½Ð¾-ÑÐ°ÑÑÐ¾ÑÐ½Ð¾ Ð¼Ð¾Ð´ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¹ ÑÐ¸Ð³Ð½Ð°Ð»
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð±ÐµÑÐ¿Ð¸Ð»Ð¾ÑÐ½ÑÐµ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÐµ ÑÑÐµÐ´ÑÑÐ²Ð°
millimeter wave radar
convolutional neural networks
deep learning
computer vision
frequency modulated continuous wave signal
artificial intelligence
autonomous vehicle

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description: The subject of the graduate qualification work is âApplication of convolutional neural networks for processing micro-Doppler signatures of continuous-wave radarsâ. The given work is devoted to the development of algorithms for processing radar data from continuous-wave radars using convolutional neural networks. The aim of the work is to develop an algorithm for recognizing micro-Doppler signatures based on convolutional neural networks, as well as to develop an algorithm for recognizing range-azimuth radar maps based on convolutional neural networks using video images as training masks. In the course of this work, deep learning methods, the principles of constructing convolutional neural networks, means of their implementation, as well as the principles of operation of FMCW radars were studied. The work was carried out using the Python language, the Tensorflow and Keras machine learning libraries, the Matplotlib library for working with graphs and images. The interactive cloud environment Google Colab was used to train neural networks. The results of the work are trained convolutional neural networks that can effectively recognize and classify unmanned aerial vehicles, cars, cyclists, and pedestrians. The developed algorithms can be used in computer vision systems to analyze data from radars. The results of the work show the possibility of using convolutional neural networks in autonomous vehicles.