title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ¸Ð³Ð½Ð°Ð»Ð° Wi-Fi
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development and experimental study of models for object recognition using a Wi-Fi signal
Creators: name: ÐÐ¾Ð¿Ð°ÑÐ¸Ð½, ÐÐ°ÐºÑÐ¸Ð¼
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: 2024
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²
Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð°
channel state information

description: ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð° ÑÐ¸Ð³Ð½Ð°Ð»Ð° Wi-Fi (Ð´Ð°Ð»ÐµÐµ CSI) Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð° Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð². Ð Ð½Ð°ÑÐ°Ð»Ðµ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸ÑÑÑ Ð¾Ð±Ð·Ð¾Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ ÑÐ±Ð¾ÑÐ° Ð¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ CSI Ð½Ð° ÑÑÐ¾Ð²Ð½ÑÑ Ð¿ÑÐ¾ÑÐ¸Ð²Ð¾Ðº Ð¸ Ð¿ÑÐ¸ÐºÐ»Ð°Ð´Ð½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼, Ð° Ð·Ð°ÑÐµÐ¼ ÑÐ°Ð·Ð±Ð¸ÑÐ°ÑÑÑÑ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿ÐµÑÑÐ¿ÐµÐºÑÐ¸Ð²Ð½ÑÐµ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð² ÑÑÐ¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸. Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ° Ð¿Ð¾ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð¼Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð² ÐºÐ°Ð½Ð°Ð»Ðµ Wi-Fi, Ð´Ð°Ð½Ð½ÑÐµ Ð´Ð»Ñ ÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ Ð·Ð°Ð¿Ð¸ÑÐ°Ð½Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ñ ÐºÐ»Ð¸ÐµÐ½Ñ-ÑÐµÑÐ²ÐµÑÐ½Ð¾Ð¹ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ¾Ð¹. ÐÐµÑÐ¾Ð´Ñ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð². Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð²ÑÐ±ÑÐ°Ð½Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð¸ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸. ÐÑÑÑÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ 87\% Ð¿Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐµ F1, ÑÑÐ¾ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÐµÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ ÑÑÐ¾Ð¶Ð¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ñ ÑÑÐµÑÐ¾Ð¼ ÑÐ°Ð·Ð¼ÐµÑÐ¾Ð² Ð¾Ð±ÑÐµÐºÑÐ¾Ð². Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ Ð¿ÑÐ¸ Ð¿ÑÐ¾ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÑÑÑÐºÑÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð° Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð²ÑÐ¾Ð´Ð½Ð¾Ð³Ð¾ ÑÐ»Ð¾Ñ Ð»ÑÑÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¸Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐ¹ ÑÐ»Ð¾Ð¹, ÑÐ°Ðº ÐºÐ°Ðº Ð¾Ð½ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¾ ÑÐ²ÑÐ·Ð¸ Ð°Ð¼Ð¿Ð»Ð¸ÑÑÐ´Ð½ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ ÑÐ¾ÑÐµÐ´Ð½Ð¸Ñ Ð¿Ð¾Ð´Ð½ÐµÑÑÑÐ¸Ñ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð¿Ð°ÐºÐµÑÐ°. Ð¢Ð°ÐºÐ¶Ðµ ÑÐ¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¾ Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð¾ ÐÐ CSIDealer Ð´Ð»Ñ Ð·Ð°Ð¿Ð¸ÑÐ¸ Ð¸ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ CSI Ð² ÑÐµÐ¶Ð¸Ð¼Ðµ ÑÐµÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸, ÐºÐ¾ÑÐ¾ÑÐ¾Ðµ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð¿Ð¾Ð²ÑÑÐ¸ÑÑ ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð¼ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð². Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð´Ð»Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÑÐ¸ÑÑÐµÐ¼ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð³ÑÐ°Ð¶Ð´Ð°Ð½ÑÐºÐ¾Ð³Ð¾ Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ.
description: The channel state information (CSI, for short) of Wi-Fi signal can be used to recognize physical objects. This work begins with an overview of solutions for collecting and processing CSI data at the firmware and application program levels, and then examines the most promising areas of research in this area. The purpose of the work is to conduct an experiment on multiple recognition of several objects in a Wi-Fi channel, the data for which was recorded using developed software with a client-server architecture. Methods of work â software development and experimentation. Convolutional and recurrent neural networks were chosen as machine learning models. The best model showed a result of 87\% according to the F1 metric, which corresponds to the results of related studies taking into account sizes of object. As a result, it was concluded that when developing the structure of a neural network for classification over time using channel state information, a convolutional layer is better suited as an input layer, since it uses information about the relationship of neighboring subcarriers to each other. The CSIDealer software also was developed for recording and visualizing CSI data in real time, which allows us to improve the quality of our experiments. The results of the work can be used to improve the quality of experiments, as well as to build multiple recognition systems for civil purposes.