# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 7 Drone Detection
### Results  
First, like in the simulations, the part of the signal responsible for uplink transmission was deleted. Then, the process of calibration was conducted. It consisted of recording the reference signal with 100% of allocated resources. Then, the Renyi entropy was calculated. The reference maximum value was 25.67 (see Figure 20). For further analysis, it was assumed that the threshold above which the signal is suitable for radar processing is 25.5.  
Using this value, the time interval for the detection was chosen. The cross-ambiguity function where the drone position could be clearly seen is depicted in Figure 21. Its bistatic range and velocity are compatible with data from GPS logs. In this case, integration time equals 20 ms.  
Measurement of the entropy allowed for the discovery that there were longer periods with enough content in the signal. Therefore, the integration time was successfully increased to the value of 100 ms without loss of the target's detection capabilities. This leads to better velocity resolution. The results are shown in Figure 22.  
Figure 19: Recorder diagram.  
Figure 20: Spectrogram of the reference signal with maximum resource allocation.  
Then, the 1-minute flight of the drone was analyzed. The trajectory of the flight is shown in Figure 18c. At the time when the Renyi entropy values were above the threshold, the radar processing was conducted. On the calculated CAFs, the CFAR detection was made. Results are shown in Figure 23. With a black line, the bistatic range and velocity from the GPS logger is drawn for reference. The red dots are the cumulative history of bistatic radar detections obtained. Integration time was set to 100 ms. This value provides sufficient velocity resolution and a convenient refresh rate for detection plots for the purposes of target tracking. At the same time, 100 ms of integration time gives a higher probability of finding the signal fragment with a sufficient Renyi entropy value. The SNR threshold was set to 15 dB.  
The proposed method allows for successful drone detection for almost the entire flight path. Thanks to the selection of those frames with a dense resource allocation, the authors confirmed the possibility of detecting UAVs in 5G-based short-range PCL applications. This would be impossible with the technique from the literature [21], since the method is not viable in dealing with the nature of 5G signals, which can be seen in Figure 24. It shows the obtained values of the Renyi entropy and the effective bandwidth computed according to [21] for 20 ms fragments across the whole 60 s signal. Values of the entropy indicate the full allocation is stable in the whole selected interval, while the effective  
Figure 21: CAF with drone detection (\(T_{\rm int}=20\) ms) with reference to its GPS position (green cross).  
Figure 22: CAF with the drone detection (\(T_{\rm int}=100\) ms) with reference to its GPS position (green cross).  
bandwidth plot shows great variability. The entropy value of approximately 25.7 shows the full resource allocation, which was constituted during the system calibration (see Figure 20). The estimated bandwidth resulting from the use of the reference method is unstable and does not cover the actual signal properties. Thus, the experiment confirmed the performance of the proposed method with real-life signals and the efficacy of drone detection using the new illuminating waveform.