title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð°Ð¼ WiFi Ð²Ð½ÑÑÑÐ¸ Ð·Ð´Ð°Ð½Ð¸Ð¹
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 of positioning technology based on WiFi signals inside buildings
Creators: name: Ð¤Ð°ÑÑÐ¸ÑÐ»Ð»Ð¸Ð½Ð°, ÐÐ·Ð°Ð»Ð¸Ñ
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: ÐÐµÑÐ¿ÑÐ¾Ð²Ð¾Ð´Ð½Ð°Ñ ÑÐ²ÑÐ·Ñ
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¼ÐµÑÐ¾Ð´ ÑÐ°Ð´Ð¸Ð¾Ð¾ÑÐ¿ÐµÑÐ°ÑÐºÐ¾Ð²
ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ
radio printing method
positioning accuracy

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description: The topic of the final qualifying work: "Development of positioning technology based on WiFi signals inside buildings". The purpose of the work is to study the accuracy of determining the location of the object of the technology being developed. The analysis of positioning methods inside buildings, positioning algorithms based on WiFi signals and the use of machine learning for positioning is carried out. Data preparation is described and several machine learning methods are compared in practice. To determine the optimal number of hidden layers and neurons, studies were conducted on the dependence of accuracy on these parameters. Experimental studies have been conducted to evaluate the chosen algorithm: the case of an empty room when using the full received power, the effect of removing points from the training dataset, adding additional information, investigating the dependence of location accuracy on the amount of training data, the case of a room with obstacles and the use of data from all three transmitters. These results are reflected in the work.Â If the final accuracy deteriorates by 1-2 m, 80% of the complete training data set can be used to train a neural network. To improve accuracy, a new feature has been added to the dataset â the number of the substate in which the point is located. As a result, the best accuracy was obtained when training the neural network on power and AOD: x_mae = 1.8 m, y_mae = 1.4 m.