Identification of Safety Categories Through Analysis of Unmanned Aerial Vehicle Trajectory Deviations

Unmanned Aerial System (UAS) is a significant part of our future with economic empowerment. Development of Electrical Unmanned Aerial Vehicles (UAV) is the framework for minimization of carbon dioxide emissions in the atmosphere. However, integration of UAVs in controlled airspace requires significant changes in airspace structure and technologies of surveillance and navigation to ensure environmental and human health. In a paper, a novel approach to safety level classification based on UAV deviation from pre-planned trajectory. Five safety categories have been used: nominal flight, minor and major deviations, hazardous situation, and catastrophic state to classify the safety level. A maximum posterior probability method is used to identify flight situation as trigger. Each safety category is specified by a particular multi-parametric normal probability density function. Results of classification could also be useful for collaborative decision-making in automatic air traffic management system. Obtained results have been verified with fixed-wind airplane trajectory data for safety category identification in the post-flight mode. Proposed approach could be useful in an Intelligent Transport System for future urban air mobility technology.