# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 2 Related Work  
Since we aim to complement and classify incomplete flight sensor data obtained from UAV sensors for a more accurate trajectory cognition, we present efforts to enhance the accuracy of flight pattern recognition or coverage path planning, such as attempts to apply DNN methods, classification approaches, and filtering algorithms for image interpolation used in our proposed method. [PERSON] et al. applied two-dimensional manifold learning to obtain individual flight patterns from UAV sensor data to classify flight fingerprinting [24]. [PERSON] et al. proposed a solution distinguishing four flight modes--ascent, descent, horizontal flight, and rotation--using a hybrid approach of UAV flight mode recognition and Gaussian Process-Unscented filter [25]. Several studies delved deep into flight trajectory patterns. [PERSON] et al. proposed predicting flight categories using long short-term memory (LSTM) autoencoder for flight trajectory patterns and generating trajectory samples through a generative adversarial network (GAN) for flight trajectory generalization [26]. [PERSON] et al. proposed an interpolation technique using optimal curvature smoothing and trajectory generation [27]. [PERSON] et al. applied extended Kalman filter to enhance trajectory tracking precision for UAVs in the maritime industry and attempted sensor fusion [28]. However, most of these studies are limited by their focus on traditional aircraft trajectories, lacking specificity for UAVs' dynamic and diverse operational patterns. For instance, [PERSON] et al. used deep autoencoder and Gaussian mixture model for aircraft trajectory clustering [29]. [PERSON] et al. classified Aeroballistic Flying Vehicle paths using artificial neural networks [30]. However, to address the huge demand in air traffic in the future, more specific aircraft trajectory classification beyond standardized normal trajectory categories has recently emerged. [PERSON] et al. attempted aircraft trajectory clustering using a circular statistic approach [31]. [PERSON] et al. proposed classifying flight patterns into nine categories using a deep sequential neural network from flight path data based on automatic dependent surveillance broadcast (ADS-B) [32]. There have been similar attempts targeting ships rather than aircraft; [PERSON] et al. introduced the Mathematical-Data Integrated Prediction (MDIP) model for predicting ship maneuvers, blending mathematical forecasting using an extended Kalman filter with data-driven predictions via least squares support vector machines, showcasing improved generalization in various maneuvering tests [33]. [PERSON] et al. presented SeaBil, a deep-learning-based system using a self-attention-weighted Bi-LSTM and Conv-1D network for ultrashort-term prediction of ship maneuvering, demonstrating higher accuracy in predicting various ship motion states compared to other methods [34]. These approaches, while effective for conventional aircraft, do not adequately address the unique challenges presented by UAVs, particularly in cluttered or unstructured environments. On the other hand, the issue of limited sensor data sampling for distinguishing flight trajectory categories leads to problems with padding or inpainting [35; 36; 37; 38]. [PERSON] et al. showed consistent effective performance of partial convolution binary masks in various tasks related to inpainting and padding [39]. [PERSON] et al. effectively applied partial convolution for complementing missing geographic information of spatial data obscured by clouds [40]. However, these methods often fail to address the severe data loss scenarios typical in UAV operations, where data accuracy is paramount. Therefore, we attempt interpolation for trajectories with high data loss to fully utilize the advantages of partial convolution previously mentioned. Attention mechanisms provide the model with the ability to selectively focus on the parts of the input that are most relevant during the learning process in deep learning architectures.