# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 4 Evaluation
### Interpolation Performance Comparison  
Our proposed methodology supports advanced technology that takes the domain of data collected from UWB from numerical values to images and fills in the holes using DNN. In Section 3.2, although the excellence of the Kalman filter in the numerical domain is recognized, we confirmed that data distortion occurs when the degree of missingness is severe. On the contrary, we also confirmed that our proposed methodology using DNN ensures stable interpolation performance in the image domain. This section lists specific quantitative evaluations of these facts discussed earlier and describes the analysis based on the results. Through this, we verify the excellence and feasibility of the proposed methodology. First, we evaluate the similarity of images derived through the proposed methodology. The dataset used for evaluation is categorized into 12 labels and consists of a total of 240 images, 20 for each label. Each label represents a unique UWB trajectory, and approximately 50% missing rates are applied to all images for evaluation. Based on this dataset, we derive the results of applying our proposed methodology and Kalman filter to a single image and measure the similarity between them and the original image using various metrics. Table 2 shows the measurement results. In the table, a row represents the average value of a single label for various metrics and columns represent Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Feature-based Similarity Index (FSIM), and \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\  & **Horizon** & **Left Down** & **Left Up** & **Mirror** & **Mirror** & **C** & **Grignal** & **Original** & **Right Down** & **Right Up** & **Square** & **Triangle** & **Vertical** \\ \hline RMSE (Proposed) & 0.0022 & 0.0034 & 0.0029 & 0.0041 & 0.0042 & 0.0035 & 0.0047 & 0.0037 & 0.0028 & 0.0047 & 0.0035 & 0.0023 \\ RMSE (Kalman filter) & 0.0138 & 0.0152 & 0.0157 & 0.0163 & 0.0176 & 0.0167 & 0.0180 & 0.0155 & 0.0151 & 0.0177 & 0.0176 & 0.0121 \\ \hline PSNR (Proposed) & 53.57 & 49.57 & 51.28 & 47.91 & 47.85 & 49.16 & 46.74 & 48.98 & 51.58 & 47.00 & 49.48 & 52.92 \\ PSNR (Kalman filter) & 37.46 & 36.40 & 36.09 & 35.79 & 35.08 & 35.54 & 34.92 & 36.22 & 36.47 & 35.05 & 35.12 & 38.63 \\ \hline SSIM (Proposed) & 0.9963 & 0.9925 & 0.9947 & 0.9903 & 0.9893 & 0.9921 & 0.9872 & 0.9915 & 0.9950 & 0.9870 & 0.9918 & 0.9963 \\ SSIM (Calman filter) & 0.9296 & 0.9191 & 0.9135 & 0.9077 & 0.8915 & 0.9025 & 0.8871 & 0.9159 & 0.9196 & 0.8893 & 0.8921 & 0.9419 \\ \hline FSIM (Proposed) & 0.7592 & 0.7330 & 0.7357 & 0.7061 & 0.7124 & 0.7245 & 0.7002 & 0.7145 & 0.7573 & 0.7048 & 0.7260 & 0.7717 \\ FSIM (Kalman filter) & 0.3748 & 0.3230 & 0.2952 & 0.2907 & 0.2678 & 0.2749 & 0.2726 & 0.2971 & 0.3267 & 0.2999 & 0.2853 & 0.4483 \\ \hline SRE (Proposed) & 62.63 & 60.52 & 61.37 & 59.61 & 59.48 & 60.22 & 58.90 & 60.22 & 61.52 & 58.97 & 60.27 & 62.30 \\ SRE (Kalman filter) & 52.49 & 51.86 & 51.69 & 51.47 & 51.02 & 51.33 & 50.92 & 51.76 & 51.89 & 50.92 & 51.02 & 53.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Image similarity results for trajectory interpolation. Signal to Reconstruction Error Ratio (SRE) between the predicted image and the original image for each interpolation technique. The description of each metric is listed as follows:  
* RMSE is an indicator that measures the difference between the value predicted by the model and the actual value. This method squares the prediction error, calculates the average, and then takes its square root. The smaller the value, the closer the model's prediction is to the actual value. It is often used in regression analysis to evaluate the prediction accuracy of a model. * PSNR is an indicator used to measure the quality of signals, such as video or voice.