Enhancing drone abnormal behavior detection using data fusion techniques and dynamic Bayesian network methods

Abstract

This study explores the increasingly complex domain of drone behavior and management within urban airspaces, particularly focusing on Europe's U-space framework. It highlights the critical evolution from basic drone detection to the essential identification of abnormal drone behaviors in the challenging and variable environments of city skies. Additionally, the paper explores the advancement of drone movement management through the application of data fusion techniques. These techniques integrate diverse sensor data to distinguish between normal and abnormal drone activities effectively. Furthermore, this research introduces a novel approach for identifying abnormal drone behaviors, leveraging data fusion methods adeptly, marking a significant step toward more reliable and robust drone management in the multifaceted, dynamic environments of urban areas. This study identifies a notable gap in current methodologies for detecting abnormal drone behaviors, particularly in the specialized application of data fusion techniques. We introduce a comprehensive approach utilizing Dynamic Bayesian Networks (DBN) as the foundation for our methodology. The integration of advanced data fusion techniques with DBNs is shown to significantly enhance the capability to identify and analyze abnormal drone activities accurately. This dual focus on DBNs and data fusion methodologies not only addresses the identified research gap but also establishes a sophisticated framework for future advancements in drone behavior monitoring. Through this work, we demonstrate the enhanced effectiveness of DBNs in drone management research, setting a new standard for precision and reliability in the field.