# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 5 Experimental Results and Discussion  
This section describes the experimental investigation of the emitter identification performance of the proposed RF fingerprinting method. Before discussing the results, several experimental setups are discussed. A custom DA system was set up for our experiments, as shown in Figure 9. The DA system consisted of a high-speed digitizer and a Raid-0 configuration with six SSD disk drives. The digitizer, PX14400, supports sampling rates of up to 400 MHz with a 14-bitanalog-to-digital converter resolution, resulting in a streaming rate of 0.7 GB/s for real-time data acquisition. With write speeds of up to 1.6 GB/s in our Raid-0 configuration, the DA system can acquire data in real-time streaming. We collected FH signals from a real experiment to determine the reliability of the algorithm. Seven FHSS devices were used to experiment. Each device utilized the same hopping rate for secure voice communication. The FH signal was frequency-modulated, and the carrier frequency was set to hops in the very high frequency range. The exact hopping rate and frequency range will not be disclosed owing to security issues. The FHSS device was connected under laboratory environmental conditions. The FH signal was acquired at a 400 MHz sampling rate and stored as raw FH data in the DA system. Target hop extraction and down-conversion were performed on the stored raw training FH data. Because we assumed the predefined hopping pattern to be known, an energy detection approach was applied to the exact hopping frequency \(f_{h}^{k}\) and the target hop samples \(\mathbf{x}_{h}^{k}\) were extracted from the observed RF signal \(\mathbf{y}\). Subsequently, the hop sample was down-converted to the baseband using a decimation factor of 20, i.e., 20M sample rate baseband hop signals \(\mathbf{s}_{h}^{k}\) were acquired. These were stored as baseband FH training data in the DA system. This down-conversion approach is reasonable because the FH signals were also demodulated to the IF or baseband to decode the digital data modulated by the message signal \(m^{k}(t)\) as in Equation (2). As the SFs depend on the component characteristics of the emitter, the SFs also should exist in the baseband hop signal, \(\mathbf{s}_{h}^{k}\). Another set of FH signals was acquired to prepare an outlier dataset. Two more FHSS devices were recruited, and the FH signals were acquired on different dates compared with those of the training dataset. The emitter specifications were the same as those of the training emitter. However, in this experiment, the FH signal was down-converted to baseband and stored as outlier FH data with a sampling rate of 2.34 MHz. For fair comparison, the sampling rate of the signal was resampled using the Fourier-domain based sampling rate conversion method, which can improve the accuracy and computational cost compared to the time domain-based method [38]. These outlier data were considered only in the outlier detection experiment described in Section 5.5. An average of 168 hop FH signals were obtained for each training emitter, and an average of 310 hop FH signals were obtained for each outlier emitter; a total of 1796 samples from nine emitters were obtained. The details are presented in Table 2.  
The results were obtained using the experimental setup as follows. For the training and testing datasets, the FH dataset was partitioned according to a 7:3 ratio; a total of 823 samples were trained, and a total of 353 samples were tested from seven emitters. In the outlier detection experiment, the test dataset for training emitters and the outlier dataset for outlier emitters were considered; a total of 353 samples from seven training emitters were tested, and a total of 620 outlier samples from two outlier emitters were tested. All the results were tested 10 times, and the average performance was presented. Figure 9: Custom-made data acquisition (DA) system. The experiments were conducted with an Intel i7-6850K CPU unit and an NVIDIA Titan RTX GPU unit.