Résumé:
Dielectric localization systems offer promising solutions for the indoor positioning of objects and people in smart environments. Traditional approaches often suffer from limited accuracy, high energy consumption, or complex infrastructure requirements, limiting their deployment in real-world applications. In this context, Bluetooth Low Energy (BLE) has emerged as a viable option for real-time localization in Internet of Things (IoT) applications, offering a good compromise between cost, energy efficiency, and availability.
This thesis explores the design, development, and validation of an indoor localization system using BLE technology, with a focus on real-time tracking, multi-device scalability, and signal processing optimization. A key aspect of the work is the implementation of angle-of-arrival (AoA)-based localization, using off-the-shelf BLE antennas and receivers. The system architecture, signal processing pipeline, and performance evaluation metrics are discussed in detail.
Several experiments are conducted in realistic indoor environments to assess localization accuracy, responsiveness, and robustness under varying conditions. Results demonstrate the feasibility of achieving sub-meter accuracy in real time, making this solution attractive for applications such as asset tracking, smart buildings, and industrial monitoring. The outcomes of this work lay the foundation for further development of distributed, energy-efficient, and scalable indoor positioning systems for IoT.
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