title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ¸Ð½ÑÐµÐ·Ð° Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
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 deep learning for the synthesis of artificial radar images
Creators: name: Ð¨ÑÐµÐ½Ð³Ð°ÑÑÑ, ÐÑÐ´Ð¼Ð¸Ð»Ð°
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: 2023
Subjects: ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
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ÑÐ°Ð´Ð°Ñ Ñ ÑÐ¸Ð½ÑÐµÐ·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð°Ð¿ÐµÑÑÑÑÐ¾Ð¹
radar images
synthesis
neural network methods
classical methods
generative adversarial network
synthetic aperture radar

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â ÑÐ¸Ð½ÑÐµÐ· ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐ¸Ð½ÑÐµÐ·Ð° Ð ÐÐ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¾Ð±Ð·Ð¾ÑÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð±ÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ Ð´Ð»Ñ ÑÐ¸Ð½ÑÐµÐ·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ð¼ ÑÐ²Ð»ÑÐµÑÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´ StyleGAN-2-ADA, Ð¿Ð¾ÑÐºÐ¾Ð»ÑÐºÑ Ð´Ð°Ð½Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð¿ÑÐ¾ÑÑ Ð² ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, Ð½Ðµ ÑÑÐµÐ±ÑÐµÑ Ð±Ð¾Ð»ÑÑÐ¾Ð³Ð¾ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¾Ð±ÑÑÐ°ÑÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ, Ð¸Ð¼ÐµÐµÑ Ð±Ð¾Ð»ÑÑÑÑ ÑÐºÐ¾ÑÐ¾ÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÑÐ°Ð±Ð¸Ð»ÐµÐ½ Ð¸ Ð¸Ð¼ÐµÐµÑ Ð±Ð¾Ð»ÑÑÐ¸Ðµ ÑÐ°Ð½ÑÑ Ð½Ð° ÑÑÐ¿ÐµÑÐ½ÑÐ¹ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ. Ð­ÑÐ¾Ñ Ð¼ÐµÑÐ¾Ð´ Ð¾Ð±ÑÑÐ°Ð»ÑÑ Ð½Ð° Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ, ÑÐ¾ÑÑÐ¾ÑÑÐ¸Ñ Ð¸Ð· 1000 Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÑ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. ÐÐ±ÑÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð¾ÑÑ Ð² ÑÑÐµÐ´Ðµ Google Colab Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð±ÐµÑÐ¿Ð»Ð°ÑÐ½Ð¾Ð³Ð¾ Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐ° NVIDIA Tesla T4. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÐºÑÐ¸ÑÐµÑÐ¸Ñ ÑÐ¸-ÐºÐ²Ð°Ð´ÑÐ°Ñ Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð³Ð¸ÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼. ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð¶Ð½Ð¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ Ð½Ð¾Ð²ÑÑ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð². ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¾ÑÐºÑÑÑÑÐµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÑÑÑÑ Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÑÑÐµÐ´ÑÑÐ²Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ (Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹) ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ python, Matlab. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¾ (Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¾) Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ðµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ Matlab.
description: The object of research is the synthesis of radar images. The purpose of the work is the development and application of an algorithm for the synthesis of radar data based on deep learning. As a result of a review of classical methods, as well as methods based on deep learning, it was concluded that the StyleGAN-2-ADA neural network method is the most suitable for synthesizing, since this method is easy to implement, does not require a large amount of training data, and has a high speed. training, stable and has a high chance of a successful outcome. This method was trained on a dataset consisting of 1000 processed radar images. Training was conducted in the Google Colab environment using a free NVIDIA Tesla T4 GPU. The results were analyzed using the chi-square test and histograms. Artificial images can be used in teaching new conservation technologies. Open educational resources and programs for searching and analyzing information were used. We used automation tools for (automated) development of Python, Matlab. Applied (tested) Matlab software.