This master’s thesis explores the development and implementation of Pedestrian Dead Reckoning (PDR) systems utilizing smartphone inertial measurement unit (IMU) sensors in conjunction with wireless technologies. The work is situated within the broader context of indoor localization, where GPS signals are unreliable or unavailable. The thesis is presented at the Cyprus University of Technology, Faculty of Engineering and Technology, Department of Electrical Engineering, Computer Engineering, and Informatics, and supervised by Michalis Michaelides. The research addresses the growing need for accurate, real-time pedestrian tracking in environments such as large buildings, transit hubs, and industrial facilities.
Integration of Smartphone IMU Sensors: The thesis leverages the accelerometers, gyroscopes, and magnetometers embedded in modern smartphones to estimate pedestrian movement. By processing raw sensor data, the system can infer step detection, heading, and displacement, forming the core of the PDR approach.
Sensor Fusion Algorithms: Advanced sensor fusion techniques are employed to combine data from multiple sensors, mitigating the effects of individual sensor biases and drifts. These algorithms are essential for filtering out erroneous readings and improving the robustness of position estimation, especially in dynamic and cluttered indoor environments.
Incorporation of Wireless Technologies: The research extends traditional IMU-based PDR by integrating wireless signals (such as Wi-Fi or Bluetooth) to provide periodic corrections to the estimated trajectory. This hybrid approach addresses the inherent drift and cumulative error in IMU-only systems, enhancing long-term accuracy and reliability.
Comprehensive Evaluation: The thesis includes experimental validation in real-world scenarios, demonstrating the effectiveness of the proposed system in accurately tracking pedestrian movement over extended periods and distances. The evaluation highlights the improvements in accuracy and robustness compared to standalone IMU-based methods.
The findings of this thesis have significant implications for the field of indoor localization and navigation. By harnessing ubiquitous smartphone sensors and augmenting them with wireless technologies, the proposed PDR system offers a cost-effective and scalable solution for real-time pedestrian tracking. This has direct applications in personal navigation, emergency response, asset tracking, and smart building management. The research also contributes to the ongoing development of sensor fusion algorithms, addressing challenges such as sensor drift, human movement variability, and environmental interference. As the demand for precise indoor positioning continues to grow, the methodologies and insights presented in this work provide a foundation for future advancements in both academic research and commercial deployment.