# A Target Detection Method of Distributed Passive Radar without Direct-Path Signal
## 4 Experiments  
In this partAccording to the parameters set in Table 1. The original signal was normalized, and then the experiment was conducted. A total of 1000 Monte Carlo experiments were conducted. The Monte Carlo experiment results are shown in Figure 4. The original signal \"1\" indicates echo signal, and \"0\" indicates no echo signal.  
The results of 1000 Monte Carlo experiments are shown in Figure 4. The experimental results show that when there are echo signals, the value of \(\xi\) is about 0.34. When there is no echo signal, the value of \(\xi\) is about 0.25. At this time, the detection threshold is 0.3 on average. When it is greater than the threshold, an echo signal is present. When it is less than the threshold, only a noise signal is present. The original results are consistent with the detection results, and the detection accuracy is 100%.  
Next, the relationship between target detection performance and SNR and signal length is verified. Each table lists the relationship between SNR and detection probability (DP). The SNR is set as \(-\)39 dB\(-\)\(-\)10 dB, the interval of each SNR change is 1, and the signal length is set as 1 ms, 10 ms, 0.1 s, and 1 s, respectively. The results are shown in Tables 2-5 and Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of receivers & 4 \\ Sampling frequency & 100 MHz \\ Destination location & (0, 50, 0) km \\ Receivers position & (10, 0, 0) km, (\(-\)10, 0, 0) km \\ Illuminator location & (0, 0, 0) km \\ Target speed & 0 \\ Signal length & 0.01 s \\ SNR & \(-\)40 dB \\ Type of signal & DVB-T \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter settings of target detection.  
Figure 4: Detection result.  
Different time delays were set, including \(-0.08\) ms, \(-0.03\) ms, \(0\) ms, \(0.03\) ms, \(0.04\) ms, and \(0.05\) ms. Monte Carlo experiments were conducted 1000 times at each time delay to verify the correctness, and the results are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of receivers & 2 \\ SNR & \(-40\) dB \\ Signal length & \(0.1\) s \\ Sampling frequency & \(100\) MHz \\ \(\left[\tau_{\min},\tau_{\max}\right]\) & \(\left[-0.1\) ms, \(0.1\) ms\(\right]\) \\ Time moving interval \(b\) & \(0.001\) ms \\ Target speed & \(0\) \\ Receivers position & \((10,\,0,\,0)\) km, \((-10,\,0,\,0)\) km \\ Illuminator location & \((0,\,0,\,0)\) km \\ \hline \hline \end{tabular}
\end{table}
Table 6: The parameter of TDOA estimation.  
Figure 5: The relationship between detection probability and SNR and signal length. (**a**) The signal length is 1 ms; (**b**) the signal length is 10 ms; (**c**) the signal length is 100 ms; (**d**) the signal length is 1 s.  
The experimental results are shown in Figure 6. It can be seen that under \(-40\) dB, the delay at the detection peak is consistent with the set delay, and the delay detection accuracy reaches 100%.  
Figure 6: The result of different TDOA estimation results. (**a**) The TDOA is \(-0.08\) ms; (**b**) the TDOA is \(-0.03\) ms; (**c**) the TDOA is \(0\) ms; (**d**) the TDOA is \(0.03\) ms; (**e**) the TDOA is \(0.04\) ms; (**f**) the TDOA is \(0.05\) ms.  
Next, the relationship between delay estimation and SNR is verified. The SNR is \(-50\)--\(-31\) dB, and the signal accumulation time is \(0.1\) s. The experimental results are shown in Figure 7.  
As can be seen in Figure 7, when \(-50\)--\(-31\) dB, the accuracy of TDOA estimation is \(1\) when the SNR is above \(-35\) dB, indicating that the method of TDOA estimation in this paper is insensitive to SNR and can adapt to more situations and lower SNR.