Advancing the Detection of Abnormal Drone Behaviors: A Dynamic Bayesian Network Approach Enhanced by the Belief Function Machine

Ensuring operational safety and compliance through effective detection of abnormal drone behaviors in urban environments is a significant challenge. This study enhances anomaly detection methodologies using Dynamic Bayesian Networks (DBNs) to model complex, temporal behaviors, addressing the lack of real observational data. We extend anomaly detection to track behaviors over time, offering a nuanced understanding of drone dynamics. A novel framework is introduced, leveraging simulated environments to generate rich datasets of normal and abnormal behaviors, integrated with data fusion techniques like the Joint Probabilistic Data Association Filter (JPDAF) to amalgamate diverse sensor inputs. Additionally, the Belief Function Machine (BFM) is incorporated to handle operational uncertainties and fuse incomplete or conflicting information, further improving the model's efficiency and realtime applicability in urban airspace management. Through rigorous experimentation, our research bridges the gap in drone anomaly detection and sets a benchmark for autonomous aerial vehicle monitoring. This work highlights the importance of advanced computational models and simulation-based testing in overcoming real-world data limitations, advancing drone safety and regulatory compliance in urban settings.