Design and Implementation of a Hybrid Localization Algorithm for Autonomous Navigation Systems

Recently, autonomous navigation in indoor environments has received increasing attention from the research community. In fact, different autonomous systems are going to be adopted also in these environments to support various applications, for instance, for logistic operations to perform some inspection and monitoring tasks in industrial plants as well as in greenhouses. In these scenarios, it is fundamental to know not only the target’s position but also its attitude. Regarding the position estimation, usually the Ultra-Wideband (UWB) technology is employed providing accurate Time of Arrival (ToA) measurements while for the attitude estimation, Inertial Measurement Units (IMU) sensors are typically used allowing to estimate roll and pitch of the target. However, the estimation of the yaw angle employing a compass sensor is too inaccurate because the Earth’s magnetic field in indoor environments is heavily affected by electric and electronic devices as well as surrounding metallic objects and structures. To overcome this limitation, the UWB technology is becoming also a promising solution to estimate the angle with which the transmitted UWB signal arrives at the receiver. Thus, exploiting this type of measurement, it is possible to estimate the yaw angle of the target. In particular, the UWB technology can be used to perform Angle of Arrival (AoA) measurements employing antenna arrays at the receiver. The focus of this thesis is to design different real-time localization solutions for indoor environments based on Ultra-Wideband (UWB) technology that enable autonomous navigation. The UWB technology can be used to perform Time of Arrival (ToA) and Angle of Arrival (AoA) measurements to estimate the position and the orientation of the tag. These measurements can be merged through an Extended Kalman Filter (EKF) to obtain a more accurate estimator. In this thesis after a comprehensive overview of the localization problem, first two localization solution that uses only ToA measurements will be designed and then a hybrid solution that combines ToA and AoA measurements. Lastly, real PDoA and ToA measurements will be presented to analyze real measurement errors.