# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 3 Result
### Simulation Data, Simulation Results, and Analysis
#### 3.1.1 Simulation Analysis of Algorithm Performance with Pure Reference Signal  
In this part, the sampling rate is set to 20 kHz and the accumulation time is 0.5 s. It is assumed that the reference channel is pure.  
Figure 2 shows the results of traditional R-D processing, and Figure 3 gives the output SNR of the algorithm presented in this paper. It can be seen from Figure 2 that traditional R-D processing can only detect Tar1, which has a high SNR, and the integration gain is about 5.8 dB, whereas the small target is submerged under the noise floor. In Figure 3, the Tar1 output SNR  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Range Unit** & **Doppler Unit** & **SNR** \\ \hline Tar 1 & 300 & 50 & \(-\)25 dB \\ \hline Tar 2 & 100 & 300 & \(-\)30 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Target Parameter.  
in this paper is about 9.4 dB and the output SNR of Tar2 is about 4.4 db. Compared with the traditional R-D algorithm, the output SNR obtained in this paper is higher.  
Figure 4 shows the difference comparison curve between the target peak value and mean noise platform value under different echo SNR with the pure reference signal. The point corresponding to each echo SNR in the figure is the result of 10,000 Monte Carlo simulations. It can be seen from the figure that the output SNR of the algorithm in this paper is much higher than that of the traditional R-D algorithm. With the increase of the echo SNR, the output SNR is improved faster.  
Figure 3: Integration results with the proposed method: \(N=10^{4}\) (**a**) Doppler dimension; (**b**) Range dimension.  
Figure 2: Integration results with traditional R-D processing: \(N=10^{4}\) (**a**) Doppler dimension; (**b**) Range dimension.  
Figure 5 presents a comparison of precision values between the proposed algorithm and the R-D algorithm. The precision is defined as the proportion of the real target samples to all the predicted target samples. Under the condition that all targets can be detected, the threshold is set to be 0.5 dB less than the minimum target peak value. It can be seen from the figure that the precision of the algorithm presented in this paper is slightly higher than that of the traditional R-D algorithm.  
Compared with Figure 5, the threshold in Figure 6 is set to be 2 dB less than the minimum target peak value. It can be seen from the figure that the precision of the algorithm in this paper is much higher than that of the R-D algorithm.  
Figure 4: Difference between the target peak and the average noise platform (\(N=10^{4}\)).  
Figure 5: Precision of target detection with different noise power values (\(N=10^{4}\)).  
There are 10 targets in simulation of Figure 7, and their SNR is uniformly distributed in [\(-40\) dB, \(-20\) dB]. Each point in the figure has been subjected to 10,000 times Monte Carlo simulations. It can be seen from Figure 7 that the estimation performance of the algorithm in this paper is higher than that of the traditional R-D algorithm.