## 5 Experiment Results and Discussion
The identification accuracy rates of the three scenes using the random forest and sequential detection methods proposed in this paper are shown in the following Table 1. The accuracy rates in the static state and LOS moving state are both 100%, with a slight drop in the NLOS moving state but still exceeding 99%, which demonstrates that the RFF feature vectors extracted by this method in different positions and different experimental scenes still have a high degree of similarity and are less influenced by the mobility environment. Then, the simulated channel is added through MATLAB to emulate a high-speed moving scene and test the recognition performance in high mobility environment. The frames captured in the static state pass through the Rayleigh channel model, and the mobile Doppler frequency shift is added. The relative moving speed between the module and the USRP is set in the range of 0-70 km/h, increasing every 10 km/h, so that the signal after going through the mobile channel with different speeds can be obtained from the output. Figure 5 below shows the RFF characteristic vectors waveforms of one frame signal of ESP8266 module No. 1 passing through the mobile environment at different speeds from 0 to 70 km/h which reveals that the extracted characteristic value curves of the same frame of signal after experiencing different mobile speeds are basically coincident and are hardly affected by the high-speed movement. Subsequently, the collected and simulated data are used to train and test to calculate the recognition rate. The training set utilizes  
Figure 4: Multiple frames superimposed RFF features of eight ESP8266 modules in indoor LOS moving state: (**a**) No. 1; (**b**) No. 2; (**c**) No. 3; (**d**) No. 4; (**e**) No. 5; (**f**) No. 6; (**g**) No. 7; (**h**) No. 8. 500 frames of received signals acquired from each module in the static position p1, while the test set utilizes the 500 frames of signals acquired in the static position p1 to p4 per module which have passed through the simulated channel after they are collected. There is no overlap between the training set samples and the initial samples of the test set that has not experienced the channel. From the test results in Table 2 below, it can be seen that the identification accuracy rate can reach 100% when the relative moving speed is below 20 km/h, and it starts to decrease slightly after the speed exceeds 30 km/h, but the accuracy rate is always above 99% when the speed is between 30-70 km/h. Thus, it can be seen that in the simulated high mobility scenario, the proposed identification mechanism based on RFF still shows strong anti-moving and anti-multipath performance and the recognition rate for eight modules of the same model can still achieve more than 99% when the vehicle speed reaches 70 km/h. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Module Number**} & \multicolumn{3}{c}{**Experimental Scene**} \\ \cline{2-4}  & **Static State** & **LOS Moving State** & **NLOS Moving State** \\ \hline
1 & 100 & 100 & 99.60 \\
2 & 100 & 100 & 100 \\
3 & 100 & 100 & 100 \\
4 & 100 & 100 & 100 \\
5 & 100 & 100 & 99.50 \\
6 & 100 & 100 & 98.70 \\
7 & 100 & 100 & 100 \\
8 & 100 & 100 & 100 \\ Overall Average & 100 & 100 & 99.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Identification accuracy rate (%) of eight ESP8266 modules based on RFF features in static and moving states. Figure 5: RFF features of one frame signal of ESP8266 module No. 1 in simulated high mobility environment. In general, according to the experiments under static and low-speed moving scenes and high-speed simulation tests, the RFF extraction method based on the preamble field features proposed in this paper can extract device fingerprint characteristics with high discrimination and stability for distinguishing and authenticating different devices in a mobile multipath environment at vehicle speed level.