# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 5 Experimental Results and Discussion
### Class Activation Map (CAM) Analysis of the DIN Classifier  
We investigated the feature map of the DIN classifier to understand why the DIN-based model works well. To this end, we applied a gradient-weighted CAM (GCAM) to visualize the feature map. The GCAM is a well-known feature visualization that identifies parts of the input signal that positively influence the class decision [40]. This can be achieved by back-propagating the gradient of the inference to the input layer and highlighting the input parts using positive gradient values. The details of the GCAM are described in Appendix C.  
The average GCAM (AGCAM) results are presented in Figure 12. Interestingly, for each emitter classification, we found that the activated region of the AGCAM is the location at which the head and tail of the signal are located. The GCAM of the positive sample with an inference score of 0.99 or higher is shown in Figure 12b. These results show that when the classifier model correctly identifies the emitter ID, the filter maps of the model are activated similarly to the AGCAM of the target emitter. In other words, the intensity of the activated region differs from that of the AGCAM, but the shape and location of the activated region are similar to those in the AGCAM results. Conversely, the GCAM of the negative sample with an inference score of 0.30 or less is shown in Figure 12c. The results demonstrate that when the model misidentifies the emitter ID, the activated region of the filter maps is completely different from those of the target emitter and other emitters.  
To verify the meaning of the activated region in Figure 12, the physical layer convergence protocol (PLCP) frame format for the FHSS network as defined in the 802.11 standard [10] is presented in Figure 13. It can be verified that the preamble field is located at the head part of the frame, and the frame body is located at the tail part of the frame.  
The preamble is a sequential signal for synchronization between the transmitter and receiver. Therefore, duplicated sync sequences must be transmitted repeatedly. When the data sequence contained in the frame is identical for each emitter, only the differences in SFs remain, which is helpful in RF fingerprinting. Consequently, many researchers have applied additional preprocessing steps to extract preamble signals [5; 6]. However, the proposed method based on the DIN classifier can automatically learn the preamble field without additional preprocessing steps.  
In the cases of the frame body, the GCAM is activated in this region because the FH signal dataset is collected in a laboratory environment; hence, the data sequences contained in the frame body are similar to each other. This similarity of the data sequences can help identify the differences in the SFs of the emitters. Again, the proposed method can automatically learn the fields in which the emitter IDs can be identified.  
Figure 12: Examples of GCAM of the DIN classifier: (**a**) AGCAM for target emitters, (**b**) positive sample with an inference score greater than 0.99, and (**c**) negative sample with an inference score less than 0.30.