# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 3 Result
### Simulation Data, Simulation Results, and Analysis
#### 3.1.2 Simulation Analysis of the Algorithm's Performance with Impure Reference Signal  
In this part, the target parameters refer to the parameter settings presented in the previous section, as shown in Table 1. The reference channel is impure. Figure 6: Precision of target detection with different noise power values (\(N=10^{4}\)). Figure 7: Comparison of ROC curve without reference noise (\(N=10^{4}\)). In Figures 8 and 9, the SNR of the reference channel is set to 0 dB, and the signal length is \(2\times 10^{4}\). In Figure 8, the output SNR of Tar1 is about 4.16 dB and that of Tar2 cannot be detected. In Figure 9, the output SNR of Tar1 is about 8 dB and that of Tar2 is about 1.5 dB. Compared with the traditional algorithm, the proposed algorithm can detect smaller targets and the output SNR is higher. Figure 10 presents the difference comparison curve between the target peak value and the mean noise platform value under different echo SNRs with 0 dB reference channel SNR. The point corresponding to each echo SNR in the figure is the result of 10,000 times Monte Carlo simulations. Compared with Figure 4, the output SNR of the algorithm presented in this paper is also much higher than that of the traditional R-D algorithm. With an increase in the echo SNR, the output SNR also improves. Figure 8: Integration results with traditional R-D processing: \(N=2\times 10^{4}\); (a) Doppler dimension; (b) Range dimension. Figure 9: Integration results with the proposed method: \(N=2\times 10^{4}\); (a) Doppler dimension; (b) Range dimension. Figure 11 presents the comparison of precision values between the proposed algorithm and the R-D algorithm with a reference SNR of 0 dB. 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, similar to the results presented in Figure 5, the accuracy of the algorithm in this paper is higher than that of the traditional R-D algorithm. However, its estimation accuracy is slightly lower than that of the pure reference channel. Compared with Figure 11, the threshold in Figure 12 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 11: Precision of target detection with different noise power values (\(N=10^{4}\)): reference \(SNR=0\) dB. Figure 10: Difference between the target peak and the average noise platform (\(N=10^{4}\)). In Figure 13, the threshold is set to be 0.5 dB less than the minimum target peak value and the reference SNR is \(-10\) dB. Compared with Figure 11, the estimation performance of the algorithm in this paper deteriorates rapidly when the reference SNR is lower. However, in the target detection of the passive radar, the reference antenna generally points directly to the transmitting base station, and its reference SNR is generally 0 dB or even higher. Therefore, in actual target detection, the estimation performance of this algorithm is generally higher than that of the traditional R-D algorithm. There are 10 targets in simulation of Figures 14 and 15, and their SNR is uniformly distributed in \([-40\) dB, \(-20\) dB]. Each point in the figure has been subjected to 10,000 Monte Carlo simulations. The SNR reference channel is 10 dB in Figure 14 and it is \(-5\) dB in  
Figure 12: Precision of target detection with different noise power values (\(N=10^{4}\)): reference \(SNR=0\) dB. Figure 13: Precision of target detection with different noise power values (\(N=10^{4}\)): reference \(SNR=-10\) dB. Figure 15. It can be seen from Figure 14 that the estimation performance of the algorithm in this paper is also higher than that of the traditional R-D algorithm. Compared with Figure 7, the results in Figure 14 are basically the same. That is, when the SNR of the reference channel is high, the estimation performance of the algorithm in this paper is basically not affected by the SNR of the reference channel.