Date of Award
Master of Science (MS)
Eric W. Frew
Small Unmanned Aircraft Systems (sUAS) are attracting significant attention for their use in a wide range applications. These applications can be categorized into two types based on their communication objective; communication-focused or communication-enabling. In both types it is beneficial to completing its mission if the sUAS is communicational-aware or it has information to assess the performance of a given communication channel. In the literature there is a lack of adaptable, robust, and online methods to provide the necessary information to be communication-aware. This thesis expands on a few authors' work to formally define and assess an online hybrid architecture that is also adaptable and robust. The architecture has a Bayesian approach combining an a-priori RF propagation model with a machine learning correction tool to provide an initial estimate and learn the deviation of that a-priori model to provide a combined prediction at any given point. The hybrid architecture is implemented and a series of assessments using simulation and flight data are performed on its capabilities and performance. The results from these assessments are that the online hybrid architecture provides a benefit over learning the field directly and when applied to a wireless airborne relaying application can perform as well as naive approaches.
Watza, Spencer G., "Assessment of an Online RF Propagation Hybrid Architecture for Communication-Aware Small Unmanned Aircraft Systems" (2018). Aerospace Engineering Sciences Graduate Theses & Dissertations. 243.