## 3 Simulation Results
### Multiple-Target Environment  
For ease of analysis, it is assumed that the power of the CUT is equal to that of the interference target, and there is no interference target in the CUT. Generally, multi-target interference can be divided into two situations: single window (leading window or lagging window) interference and double windows interference. The average detection probability of each detector in a multiple-target environment is shown in Table 6.  
As presented in Table 6, KLQVI-CFAR achieves an average detection probability that exceeds that of VIHCEMOS-CFAR by 8.17% when three interferences are present in the leading window and SNR ranges from 5 dB to 20 dB. With four interferences in the lagging window and SNR range of 0 dB to 35 dB, the improvement reaches 15.58%. Additionally, when one interference exists in both the leading and lagging windows and SNR is between 20 dB and 35 dB, KLQVI-CFAR outperforms VIHCEMOS-CFAR by 1.31%. These results demonstrate that the KLQ algorithm implemented in KLQVI-CFAR offers superior detection performance in multi-target environments compared to the MOS algorithm used in VIHCEMOS-CFAR. Furthermore, under conditions of environmental misclassification, the FOSTA algorithm in KLQVI-CFAR provides greater robustness than the HCE algorithm in VIHCEMOS-CFAR. Moreover, when three interferences are located in the leading window, KLQVI-CFAR's average detection probability is 3.40% and 11.78% higher than that of IBQ-CFAR and OS-CFAR, respectively. In the case of four interferences in the lagging window, the respective improvements are 2.93% and 5.86%. When one interference exists in each of the leading and lagging windows, KLQVI-CFAR consistently outperforms IBQ-CFAR, OS-CFAR, and KLQ-CFAR. Owing to its adaptability, KLQVI-CFAR also achieves significantly better detection performance than CA-CFAR in multi-target scenarios. Among the three scenarios, the case with one interference in both the leading and lagging windows is selected as a representative to validate the significance of performance differences between KLQVI-CFAR, KLQ-CFAR, and VIHCEMOS-CFAR. The ANOVA results for KLQVI-CFAR, KLQ-CFAR, and VIHCEMOS-CFAR are shown in Table 7.  
As shown in Table 7, in this environment, \(F\) for KLQVI-CFAR, KLQ-CFAR, and VIHCEMOS-CFAR is 618.5880, and \(P\) is 2.812 \(\times\) 10\({}^{-23}\), with \(P\) being less than 0.05, indicating that the superior performance of KLQVI-CFAR is not due to chance. In order to further illustrate the negative effect of environmental misjudgment in multiple-target environments, environmental misjudgment probabilities are given as shown in Figure 5. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Environment** & **CA** & **KLQ(6,3)** & **OS(28)** & **IBQ(6,3)** & **VIHCEMOS** & **KLQVI(6,3)** \\ \hline Three interferences in the leading window & 0.1189 & 0.3492 & 0.3128 & 0.3382 & 0.3233 & 0.3497 \\ Four interferences in the lagging window & 0.1068 & 0.5270 & 0.5003 & 0.5145 & 0.4582 & 0.5296 \\ One interference in each of the leading and lagging windows & 0.4029 & 0.9414 & 0.9341 & 0.9395 & 0.9305 & 0.9427 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of average detection probability of each detector in multiple-target environments. Figure 5a illustrates that when the SNR is between 5 dB and 20 dB, the average rate at which multiple-target environments are misclassified as clutter edge environments reaches 28.87%. Similarly, Figure 5b reveals that this misclassification rate can average 27.48%, peaking at 66% when the SNR is 9 dB. Additionally, Figure 5c shows that when a single interference exists in both the leading and lagging windows, the probability of misclassification is relatively low, when the SNR exceeds 20 dB, the impact of environmental misjudment becomes minimal, and the performance of KLQVI-CFAR and VIHECMOS-CFAR is largely dependent on the detection algorithm used for multiple-target scenarios.