Indoor Positioning Based on Wi-Fi and Bluetooth Low Energy, M.Sc. Thesis Sharif University of Technology ; Shah Mansouri, Hamed (Supervisor)
Abstract
Indoor positioning plays a pivotal role in a wide range of applications, from smart homes to industrial automation. In this thesis, we propose a comprehensive approach for accurate positioning in indoor environments through the integration of existing Wi-Fi and Bluetooth Low Energy (BLE) devices. The proposed algorithm involves acquiring the received signal strength indicator (RSSI) data from these devices and capturing the complex interactions between RSSI and positions. To enhance the accuracy of the collected data, we first use a Kalman filter for denoising RSSI values, then categorize them into distinct classes using the K-nearest neighbor (KNN) algorithm. Incorporating the filtered...
Cataloging briefIndoor Positioning Based on Wi-Fi and Bluetooth Low Energy, M.Sc. Thesis Sharif University of Technology ; Shah Mansouri, Hamed (Supervisor)
Abstract
Indoor positioning plays a pivotal role in a wide range of applications, from smart homes to industrial automation. In this thesis, we propose a comprehensive approach for accurate positioning in indoor environments through the integration of existing Wi-Fi and Bluetooth Low Energy (BLE) devices. The proposed algorithm involves acquiring the received signal strength indicator (RSSI) data from these devices and capturing the complex interactions between RSSI and positions. To enhance the accuracy of the collected data, we first use a Kalman filter for denoising RSSI values, then categorize them into distinct classes using the K-nearest neighbor (KNN) algorithm. Incorporating the filtered...
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