title: ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð° Ð² ÑÐµÑÑÑ Wi-Fi
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: Classification of the human position based on the channel state information in Wi-Fi networks
Creators: name: Ð¡ÑÑÐ°Ð½Ð¾Ð², 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: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ
Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð°
ofdm
wi-fi
machine learning
position classification
channel state information

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð° Ð² ÑÐµÑÐ¸ Wi-Fi. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð° Ð² ÑÐµÑÑÑ Wi-Fi. Ð Ð¿ÑÐ¾ÑÐµÑÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÑÐ²ÐµÐ´ÐµÐ½Ð¸Ñ Ð¾Ð± Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð° (CSI), Ð° ÑÐ°ÐºÐ¶Ðµ Ð½ÐµÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð½ÑÐµ ÐµÐ¹ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ»Ð¾Ð²ÐµÑÐµÑÐºÐ¾Ð¹ Ð´ÐµÑÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸, Ð¿Ð¾Ð´ÑÑÐµÑ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð´ÐµÐ¹ÑÑÐ²Ð¸Ð¹ Ð¸ Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ. Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÐµ Ð² ÑÐ°Ð±Ð¾ÑÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ ÑÑÐ¸ÑÐµÐ»ÐµÐ¼: Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ k Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹, Ð¼ÐµÑÐ¾Ð´ Ð»Ð¾Ð³Ð¸ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸, Ð»Ð¸Ð½ÐµÐ¹Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ Ð¸Ñ ÑÐ¾Ð±ÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ, Ð²Ð»Ð¸ÑÑÑÐ¸Ðµ Ð½Ð° ÑÑÐµÐ¿ÐµÐ½Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. Ð ÑÐ¾Ð´Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ° Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ Ð´Ð°Ð½Ð½ÑÐµ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ð¸ ÐºÐ°Ð½Ð°Ð»Ð° (CSI), Ð²ÑÐ´ÐµÐ»ÐµÐ½Ñ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, Ð½Ð° Ð¸Ñ Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¾ Ð²Ð»Ð¸ÑÐ½Ð¸Ðµ Ð¿ÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ, Ð½Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ¾Ð±ÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð½Ð° ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ. ÐÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð° Ð½Ð°ÑÑÑÐ¾Ð¹ÐºÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð¿Ð¾ ÐºÑÐ¸ÑÐµÑÐ¸Ñ Ð¼Ð°ÐºÑÐ¸Ð¼ÑÐ¼Ð° Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ. ÐÑÑÐ²Ð»ÐµÐ½Ð¾, ÑÑÐ¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð¼, Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÑÑÐ¸Ð¼ Ð½Ð°Ð¸Ð±Ð¾Ð»ÑÑÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ° Ð² ÐºÐ°Ð½Ð°Ð»Ðµ Wi-Fi â 96,1 %, ÑÐ²Ð»ÑÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ k Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹.
description: The subject of the graduate qualification work is channel state information in Wi-Fi network. The given work is devoted to the construction of a human position classifier based on channel state information in Wi-Fi networks. During the study, theoretical intelligence about channel state information (CSI) was considered, as well as some existing studies devoted to it: recognition of human activity, counting the number of actions and their classification. Supervised machine learning algorithms used in the work are considered: the k nearest neighbors algorithm, the logistic regression method, the linear support vector machine, as well as their own parameters that affect the fitting degree and classification accuracy. During the experiment, channel state information (CSI) was obtained, statistical features for classification were extracted, also based on them a classifier was built using several machine learning algorithms. The influence of data preprocessing was studied, a set of features, as well as the own parameters of algorithms on the accuracy of the correct position determination. The classifier was tuned according to the criterion of the maximum probability of correct position determining. It was found that the algorithm that provides the highest classification accuracy of a person's position in the Wi-Fi channel - 96.1 %, is the k nearest neighbors algorithm.