# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 5 Conclusions  
Our proposed framework, integrating a distance-based UWB positioning system and advanced image processing technologies, provides a comprehensive solution for accurate UAV trajectory tracking. The limitations faced by conventional GPS methods in complex environments and the difficulty of visual observation under various conditions require innovative approaches for UAV maneuver detection. Therefore, we propose a new solution that utilizes UWB technology to accurately track and classify UAV trajectories in real time, even in complex environments. It also improves the reliability of abnormal maneuvering detection approaches by addressing data quality problems caused by external disturbances through image-based data interpolation. Moreover, the introduction of STEAM, a novel attention mechanism specifically designed for UAV trajectories, is a key innovation in our framework. It efficiently highlights important trajectory features with high precision and less complexity, outperforming existing models on classification tasks. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Data Missing Rate (0)** & **Data Missing Rate (30)** & **Data Missing Rate (50)** & **Data Missing Rate (70)** \\ \hline hor & 100 & 100 & 100 & 100 \\ \hline ld & 100 & 100 & 100 & 100 \\ \hline lu & 100 & 100 & 100 & 100 \\ \hline mc & 100 & 100 & 100 & 100 \\ \hline ms & 100 & 100 & 100 & 100 \\ \hline ocs & 100 & 100 & 100 & 100 \\ \hline rd & 87.5 & 100 & 75 & 100 \\ \hline ru & 87.5 & 100 & 100 & 50 \\ \hline squu & 100 & 100 & 75 & 50 \\ \hline tri & 100 & 100 & 100 & 100 \\ \hline ver & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of classification of each label separated as data missing rate. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline hor & 1.00 & 1.00 & 1.00 \\ \hline ld & 1.00 & 1.00 & 1.00 \\ \hline lu & 1.00 & 1.00 & 1.00 \\ \hline mc & 0.91 & 1.00 & 0.95 \\ \hline ms & 0.95 & 1.00 & 0.98 \\ \hline occ & 1.00 & 1.00 & 1.00 \\ \hline os & 1.00 & 1.00 & 1.00 \\ \hline rd & 1.00 & 0.90 & 0.95 \\ \hline ru & 0.95 & 0.90 & 0.92 \\ \hline squu & 1.00 & 0.90 & 0.95 \\ \hline tri & 0.91 & 1.00 & 0.95 \\ \hline ver & 1.00 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of classification utilizing proposed system. Our framework's experimental results underscore its effectiveness in UAV trajectory tracking, demonstrating robust performance independent of raw data quality. Moreover, the framework's validation using a lightweight learning model emphasizes its computational efficiency while maintaining exceptional classification accuracy. We have several research plans for future work. First, we aim to explore the integration of supplementary data sources, such as infrared or LiDAR sensors, to enhance the comprehensive understanding of UAV trajectories, especially in challenging environments or adverse weather conditions. Second, evaluating the scalability and generalization capabilities of the proposed framework across diverse UAV platforms, mission types, and operational contexts will provide valuable insights into its broader applicability and performance under varied scenarios. Lastly, leveraging trajectory data from multiple UAVs, we intend to construct a framework capable of gaining extensive insights into complex flight trajectories formed by clusters of drones, offering a nuanced understanding of collaborative aerial maneuvers. By pursuing these future research directions, we aim to further advance the effectiveness and versatility of the proposed framework, addressing emerging challenges and extending its practical utility in diverse real-world scenarios. Conceptualization, K.K., D.L., T.Y. and N.Y.; methodology, K.K., D.L., N.Y. and H.K. ; software, K.K., D.L. and N.Y.; validation, T.Y., H.J. and H.K. ; formal analysis, K.K., D.L., N.Y. and H.K. ; investigation, T.Y. and H.J. ; resources, K.K., D.L., T.Y. and N.Y.; data curation, T.Y. and H.J.