# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 4 Simulation and Experiment
### Experiment  
To test the feasibility of the ILSN, the UAV experimental system was built based on DJI Mini 3 pro, which has an intelligent obstacle avoidance and high-resolution map transmission function. Based on this, we introduced IMU and gimbal, etc., which obtained more stable data images. As illustrated in Figure 7, these include the sensing system, flight control system, and power system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Robustness** & **EAO** & **AFR** \\ \hline ILSN & 0.811 & 0.244 & 0.417 & 112 \\ KCF & 0.647 & 0.773 & 0.134 & 150 \\ MCNN & 0.783 & 0.585 & 0.289 & 90 \\ SiamRPN & 0.748 & 0.460 & 0.244 & 130 \\ DaSiamRPN & 0.751 & 0.344 & 0.326 & 120 \\ SiamRPN++ & 0.781 & 0.236 & 0.414 & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the comparative experiment under 4 different scenarios. Figure 6: Accuracy comparison of the UAV123 dataset under 4 different scenarios. (**a**) Backgrounds clutters; (**b**) Partial occlusion; (**c**) Background clutters; (**d**) Similar object. The perception system consists of a camera, radar, IMU and gimbal, etc. ; the flight control system includes a server, flight body, monitor, etc. ; the power system includes a drive unit, flight battery, and DJI Ronin 4D. Table 5 illustrates the hardware and software devices. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Name** & **Parameters** & **Application** \\ \hline \multirow{8}{*}{Aircraft system} & \multicolumn{3}{c}{Volume: 171 mm \(\times\) 245 mm \(\times\) 62 mm} \\  & UAV body & \begin{tabular}{c} Speed: 5 m/s \\ Weight: 249 g \\ PTZ: 360\({}^{\circ}\) \\ Frequency: 5.725-5.850 GHz \\ Positioning: GPS+BeiDou \\ WIFI protocol: WIFI 6 \\ Model: Lenovo ST558 \\ CPU: Xeo \\ \end{tabular} & The aircraft \\  & Sever & \begin{tabular}{c} GPU: RTX 3080 \(\times\) 2 \\ RAM: 256 G \\ ROM: 1 T \\ \end{tabular} & Store and process data \\ \hline \multirow{8}{*}{Aircraft system} & \multicolumn{3}{c}{Image sensor: 1/1.3 inch, up to 48 million pixels} \\  & Camera & \begin{tabular}{c} Angle of view: 82.1\({}^{\circ}\) \\ Equivalent focal length: 24 mm \\ \end{tabular} & Collecting all the displacement \\  & \
\begin{tabular}{c} Aperture: f/1.7 \\ Model: RD2484 R \\ Measuring range: 1.5–50 m \\ FOV: horizontal 360\({}^{\circ}\), vertical \(\pm\) 45\({}^{\circ}\) \\ Model: Honeywell HMS-MM-10 \\ \end{tabular} &
\begin{tabular}{c} Adalancing the UAV attitude. \\ Bandwidth: 200 Hz \\ Image transmission quality: 1080 p/30 fps \\ Bandwidth: 1.4 Mhz-40 Mhz \\ Real-time rate: 18 Mbps \\ Transmission distance: 12 km \\ Antenna: four \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The UAV system parameters. Figure 7: UAV system. The stable flight of the UAV system is guaranteed by the gyroscope and the gimbal. We chose eight different temporal, spatial, and altitude data acquisition scenarios, covering various scenes such as cities, mountains, streets, and lakes, in which we first set up the server data acquisition environment and set up the monitor and UAV communication channels. The UAV system dynamically captures low-altitude images based on the ultra-high-definition binocular vision camera, which is controlled by the monitor. The monitor allows us to dynamically adjust the speed and height of the drone to increase the variety of samples collected. The collected data were transmitted to the server and monitored through the image transmission module. The server stores and processes the data according to the obtained images, completes dynamic target recognition, and transmits the data to the flight control system to complete the target tracking. Then, we obtained 1000 sets of image data for each of the 8 scenarios through post-processing. An example of the collected experimental data is shown in Figure 8, which shows the target recognition and tracking images of 8 scenes. Figure 8: Experimental data.