# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 1 Introduction  
Unmanned aerial vehicles (UAVs) can perform a variety of important tasks, replacing human labor in tasks such as escort, patrol, security, delivery, and search and rescue [1]. The optimal UAV flight trajectory may differ depending on the purpose of the mission performed by the UAV [2]. Accurately flying the predefined flight trajectory appropriate for the purpose of the mission are very important in terms of the purpose and efficiency of UAV operation [3, 4]. However, it can be difficult to quickly and accurately check the flight trajectory [5, 6]. The most basic conventional method to determine the location of a UAV is to use Global Positioning System (GPS), which utilizes satellites. However, GPS has the disadvantage that reception signals can be weak in complex urban terrain and cannot be used in indoor environments covered by architectural structures [7]. The way to check UAV flying with the naked eye is difficult to grasp the overall trajectory if the distance from the UAV increases, and it can be difficult to recognize UAV in low-light environments. Lastly, the method of attaching a camera to a UAV to determine the location of a UAV may also be incomplete due to unstable network connections in disaster situations, which are environments where UAVs are mainly used [8, 9]. As UAVs often have to be operated in environments with incomplete network connectivity and have high mobility, frequent network communication losses mayoccur during flight. In addition, the flight data recorder (FDR) of flight controllers such as ArduPilot Mega (APM) and Pixhawk can be used to record flight history by utilizing the UAV's own sensors, but accurate reproduction of 3D trajectories is limited by empirical problems, such as sampling frequency constraints or network delay and loss [10]. The ultra-wideband (UWB) positioning system is a range-based positioning method with relatively simple infrastructure configuration and high accuracy. The strength of UWB systems is that they use high frequency bandwidth and have low signal interference. Additionally, the UWB system has the advantage of enabling high-precision positioning using high nanosecond time resolution and can be effectively used both indoors and outdoors over long distances [11]. In addition, image processing technology is one of the core technologies widely used in the autonomous driving, medical, security, and entertainment industries by configuring and optimizing nonlinear modeling through a multi-layer neural network structure [12; 13]. Recently, practical research is being conducted on unmanned aerial vehicles (UAVs), one of the many fields where such image processing can be utilized [14; 15; 16; 17]. Among the many ongoing studies, there are increasing attempts to image the flight trajectory of a UAV and use it for performance verification [18] and failure diagnosis [19]. In general, UAV flight trajectory records are a key element in the UAV mission performance verification process to evaluate the accuracy and efficiency of UAV mission performance [20; 21; 22; 23]. However, if frequent network communication loss occurs, a large portion of the flight trajectory records used to verify the UAV's mission performance may be lost, making accurate evaluation impossible. Therefore, in this study, we design a system that tracks the position of a UAV in real time by introducing location determination using this UWB positioning system into the UAV trajectory tracking process. Additionally, we propose a framework that can interpolate and evaluate UAV flight trajectories without loss by utilizing a deep interpolation model and spatial trajectory enhancement attention mechanism (STEAM), even in a limited network infrastructure environment based on UAV flight trajectory images. The proposed system involves several key steps. First, we introduce a deep neural network (DNN) technique to interpolate missing data based on images.