## 3 Results and Discussion
### Drone Detection
#### 3.1.2 Signal 2 (Test 2)  
Figure 12 illustrates the graph at point A and B in Figure 8, respectively, for the next run of a drone flying and crossed the FS baseline. In this run, the drone starts to enter the FS main lobe after approximately 8.3 s. The direct signal disrupted spans for an almost 1.5 s duration (8.3-9.8 s); this indicates that the drone has a slightly higher speed compared to the previous signal 1. Consequently, the Doppler frequency value is slightly higher (approximately reach 91 Hz) than signal 1. Nevertheless, the sample duration was taken from 6-14 s for STFT processing. The captured signal, once again, proved that the passive FSR DVB-S system can detect a target flying crossing the FS baseline. Figure 10: _Cont._  
Figure 10: Time domain and STFT plot of IMF1-IMF17. Correspondingly, the next processing is to decompose the detected signal by using EMD to extract frequency components contained in the received signal. In contrast, the resultant sifting process terminated at 16 IMFs as presented vectors in Figure 13, and a few points can be depicted:  
1. The time domain envelope of the extracted vectors for IMF1-IMF4 are approximately similar;
2. IMF1-IMF2 visibly show the higher frequency component to the original signal frequency value, with far greater than the 91 Hz; hence, it is considered to represent the noise component in the signal;
3. IMF3 and IMF4 represent the micro-Doppler components with the main Doppler underneath the lower frequency level;
4. IMF5-IMF7 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, IMF5 provides a clear signature of the target crossing baseline-- this will be further analyzed in the subsequent section;
5. The Doppler frequencies in IMF8-IMF16 are 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. Figure 11: Extracted Doppler Component IMF6 (**a**) time domain and (**b**) its corresponding STFT plot. Figure 12: (**a**) Received signal (Signal 2) in time domain at the output of ADC, and (**b**) its joint time–frequency representation of the sampled signal (6–14 s) denoted as target signature. Figure 13: _Cont._  
Figure 13: Time domain and STFT plot of IMF1-IMF16. Once again, the extracted vector signatures by EMD are more refined and confirmed by the detection of a flying drone based on the Doppler and micro-Doppler components present. Both the Doppler, due to the drone linear motion, and the micro-Doppler, due to the rotating blades, can be distinguished as further analysis on IMF5, as presented below in Figure 14 for Signal 2. A complete passive FSR system based on DVB-S of Measat3 has successfully developed and implemented. The system can detect a commercial drone fly and crossed the FS baseline between the satellite and ground station. It also revealed that the bistatic RCS at FS mode is the highest irrespective of the blade orientation and its material. The SNR of the direct path was considered reasonable to make detection possible with 1 dB above the detection threshold of 13 dB for passive bistatic application. By using Equation (5), the P\({}_{\text{dir}}\) arriving the antenna front end is \(-82.13\) dBm, which is within the practical value range, hence required a processing gain of 55-75 dB to achieve the minimum SNR for detection--the detail of the analysis can be seen in [9]. \[Prx\_bs=\left(\frac{EIRP.Gr.\lambda^{2}.\mathit{ob}}{\left(4\pi\right)^{3}Rx^ {2}Rx^{2}}\right) \tag{5}\]  
With the enhanced performance of the FSR receiving system, the minimum processing gain of 10-20 dB is enough to meet the sensitivity level of the receiver amplitude detector, hence the manifestation of the improved FSR capability.