## 5 Experiment Results and Discussion
Partition NLOS moving state: The experimenter pushes the mobile trolley to randomly walk along the corridor separated by a wall with the laboratory as shown in Figure 3. The movement form is also an average of 1 m/s uniform motion with random moving path and moving direction including straight lines, curves, circles and other motion modes. In the whole partition wall experiment, there is no direct path between the receiving device and the transmitting device, and the signal needs to be transmitted through the wall. The experimental data in this state can be used to verify the devices classification and identification effect based on RFF features under the NLOS condition. In the above three scenes, after the PPDU signal frames of eight modules are collected and captured, time synchronization and frequency offset compensation are completed for each frame of signal according to the preprocessing process described in Section 3. Then, the RFF feature vectors are extracted and divided into training set and test set to classify and identify different modules according to the method introduced in Section 4, and the identification accuracy in different experimental scenes is calculated and analyzed to verify the feasibility and superiority of the proposed RFF-based DSRC devices identification mechanism. Firstly, the collected data in LOS moving state are taken as an example, and the RFF characteristic vectors of eight ESP8266 modules are drawn in Figure 4. Each module is selected 100 frames of signals, and the feature vectors extracted from each frame of each module are superimposed and drawn on the same figure. As can be seen from the figure, the amplitudes in different dimensions of the eigenvector of different modules are different, and the waveform trend and concave-convex characteristics also have obvious differences, indicating that the module RFF characteristics of the same model also have a certain degree of differentiation. On the other hand, the waveform trends of features extracted from the multiple frames of the same module in the mobile multipath environment are basically the same. After superimposing the characteristic curves of a plethora of signal frames on the same figure, it can be approximatively regarded as falling on the same curve, which indicates that the fingerprint characteristics of the same module are still relatively stable and consistent in the mobile environment and are less affected by the channel. Next, the extracted RFF feature vectors are trained and tested, and the identification accuracy is used for quantitative comparison and analysis. The 500 PPDU frames per module collected in position p1 are used as the training set, and the 500 frames collected in position p2 to p4 in the static state, 500 frames collected in indoor LOS moving state, and 1000 frames collected in partition NLOS moving state per module are used as the test set. Only the feature vectors of the signals in a fixed position are used for training, and the features of the signals acquired in the remaining fixed positions and moving scenes are used for testing, which can verify that the fingerprint features are independent of position and moving state. The selection of the training set and test set samples above also corresponds to the practical application scenarios. Generally speaking, signal acquisition and training in a static position is achievable, but the moving situation of the V2X devices in the actual working state is variable, which results that the channel and environment during identification are different from those during training.