## 3 Results and Discussion  
### Drone Detection  
#### 3.1.1 Signal 1 (Test 1)  
Figure 9 illustrates the time domain of the received signal output of ADC and its corresponding STFT representation at points A and B in Figure 8, respectively. In this scenario, a 15 s observation time was considered; hence, the recording signal can show the moment that the drone crosses the baseline. The drone flies and crosses the baseline approximately at time 7.5 s, which can be seen in Figure 9a. The direct signal disrupted spans for an almost 2 s duration (7.5-9.5 s). Hence, the captured signal was sampled between 5-13 s for STFT to minimize the processing time, as shown in Figure 9b, and defined as a target signature. The signature comprises both the main Doppler, due to the linear motion of the body, and the micro-Doppler, due to the rotating blades, which also indicates the highest Doppler frequency value reaches to 88 Hz. It can be assumed that the micro-Doppler occupied the higher frequency region. This result proved the p-FSR DVB-S system can detect a flying target crossing the FS baseline.  
Next, the detected signal is decomposed using EMD to extract all frequency components present in the signal. These IMFs are not meant to classify the target among other low-profile targets like birds, but rather confirmed that the detected target is a drone-based on the unique signature is from the  
Figure 9: (**a**) Received signal (Signal 1) in time domain at the output of ADC, and (**b**) its joint time–frequency representation of the sampled signal (5–13 s) denoted as target signature.  
STFT figure where an expected V-shape signature made by the crossing drone is surrounded by the micro-Doppler due to the rotating blade, as proved in [10] for the FSR system. In total, 17 corresponding frequency components were extracted from the main signal and illustrated in Figure 10 (point C in Figure 8); a few points can be depicted:  
1. The time domain envelope of the extracted vectors for IMF1-IMF7 are approximately similar;
2. IMF1-IMF3 have higher frequencies. Besides, IMF2 and IMF3 visibly show the higher-frequency component to the original signal frequency value. The IMFs' component value is by far greater than the 88 Hz, hence considered to represent the noise component in the signal;
3. Similarly, IMF4 and IMF5 represent the micro-Doppler components with the main Doppler underneath the lower frequency level;
4. IMF6-IMF8 follow suit, despite the low-frequency components, yet it maintained the signal behavior. The observed signature corresponds to the main Doppler component generated by the target/drone body. Although the main Doppler components can still be surrounded by the micro-Doppler spikes, IMF6 provides a clear signature of the target crossing baseline--this will further be analyzed in the subsequent section;
5. Doppler frequency in IMF9-IMF17 is linearly spread over the lower boundary of the frequency value, making the vector components approaches a residue condition;
6. The sifting process continues until the residual condition was achieved, i.e., the minimum extreme returned negative and the maximum extreme returned positive; these makes the signal mean Y(\(\upmu\)) value equal to zero, then Hi(n) = Y(n);  
In summary, the extracted vector signatures by EMD are more refined and confirmed the detection of a flying drone based on the Doppler and micro-Doppler components present. Both Doppler due to the drone linear motion and the micro-Doppler due to the rotating blades can be distinguished as further analysis on IMF6, as presented below in Figure 11.