Deep Learning Methodologies for UWB Ranging Error Compensation

Ultra-Wideband (UWB) is being extensively introduced in various kinds of both human and robot positioning systems. From industrial robotic tasks to drones used for search and rescue operations, this high-accuracy technology allows to locate a target with an error of just a few centimeters, outperforming other existing low-cost ranging methods like Bluetooth and Wi-Fi. This led Apple to equip the latest IPhone 11 with an UWB module specifically for precise localization applications. Unfortunately, this technology is proved to be very accurate only in Line-Of-Sight (LOS). Indeed, performances degrade significantly in Non-Line-Of-Sight (NLOS) scenarios, where walls, furniture or people obstruct the direct path between the antennas. Moreover, reflections constitute an additional source of error, causing the receiver to detect multiple signals with different delays. The aim of this thesis is to compensate NLOS and multi-path errors and to obtain a precise and reliable positioning system, allowing the development of several service robotics applications that are now limited by unsatisfactory accuracies. Another fundamental goal is to guarantee good scalability of the system to unseen scenarios, where even modern mitigation methods still fail. For this scope, a large dataset is built, taking both LOS and NLOS measurements in different environments and experimenting with different types of obstacles. Then, modern Deep Learning methods are used to design a Convolutional Neural Network that predicts the error of the range estimates directly from raw Channel Impulse Response (CIR) samples. Finally, a positioning test is conducted to verify the effectiveness of the method in a real scenario.