AbstractThis thesis investigates a propagation modelling approach to navigation and source localization. Complex urban propagation environments give rise to severe multipath which impairs the reliability of conventional satellite and terrestrial based localization systems. The motivation is the development of a location determination scheme exploiting multipath propagation. In this thesis a new ray tracing method has been developed to eciently determine channel characteristics. Using a database of channel characteristics, a model is constructed to determine location of a receiver based on a matching algorithm. This thesis also investigates into the inverse problem, i.e., the
source localization in urban environments. These methods have been tested against noise and perturbations and is shown to be robust. In the simulated urban environments, navigation errors are typically less than 15m. The proposed source localization algorithm is able to locate a radio source to better than 100m. To evaluate these algorithms a 2.5D ray launching model has been developed, which is able to make use of existing digital map databases. The accuracy of this model has been validated by comparing simulated Received Signal Strengths (RSS) against channel sounding measurements in the city center of Munich, Germany. The ray launching model makes use of parallel computing techniques using Graphic Processing Units (GPU).
Key to the success of these methods is a new ngerprinting technique which correlates abstract electromagnetic features with physical coordinates. This takes advantage
of data mining and machine learning to study patterns in the ngerprint distribution. The thesis details the implementation of the navigation and source localization algorithms. In particular, consideration is given to the problem of source localization using a small Unmanned Aerial Vehicle (UAV). In order to eciently solve this problem, the technique of Dynamic Time Warping (DTW) has been explored. All errors have been quantied and their sensitivities are determined.
|Date of Award||13 Feb 2019|
|Supervisor||Peter Shepherd (Supervisor) & Robert Watson (Supervisor)|