In recent years, the rapid advancement of Unmanned Aircraft Systems (UAS) has led to an increasingly complex National Airspace System (NAS), necessitating a comprehensive understanding of factors that impact pilot visual acquisition and detection of other aircraft (including manned fixed-wing, rotorcraft, and UAS). The objective of this study is to investigate factors that affect pilot performance in visually acquiring and detecting other manned-fixed wing aircraft and manned rotorcraft using a multi-method approach, incorporating qualitative and quantitative data analysis. A diverse sample of pilots with varying flight experience participated in the study. Participants were exposed to a series of flight test scenarios in a high-fidelity flight test campaign using different flight paths and detecting different types of aircraft, designed to replicate real-world airspace encounters with other aircraft. Post-flight interviews were conducted, and situational awareness questionnaires and NASA Task Load Index (NASA-TLX) were administered to capture insights on the pilots’ experiences. The goal was to determine the level at which aircraft characteristics, test subjects’ situational awareness and workload, flight conditions, and environmental conditions influenced visual acquisition and detection. All interviews were subjected to several cycles of meticulous coding and subcoding processes to discern both individual and co-occurring factors affecting visual detection capabilities. Additionally, a rigorous statistical analysis was executed on the data derived from the situational awareness questionnaires and NASA-TLX to extract quantitative insights into pilot-centric metrics influencing visual detection. The amalgamated results from both the qualitative and quantitative analyses were synthesized to construct a comprehensive representation of all variables influencing visual detection, in addition to delineating the parallels between factors that affect visual acquisition in both manned fixed-wing and rotorcraft detection scenarios.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-7009 |
| Date | 08 December 2023 |
| Creators | Bassou, Rania |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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