# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 5 Simulation Experiment Results and Analysis
### CFVI-CFAR Detection Performance
#### 5.2.2 Multiple-Target Environment  
In a multiple-target environment, the probability of determination of the type of environment of VI-type detectors is shown in Figure 5. Figure 5a-c show the probability of determining the type of environment of the VI-type detectors under different environments:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(P_{d}\) = 0.5 & VIHCEMOS & CFVI & CA & OS & KLQ & Ideal \\ \hline SNR/dB & 13.89 & 13.87 & 13.69 & 14.29 & 13.91 & 12.77 \\ \hline \end{tabular}
\end{table}
Table 10: The SNR of each detector under the same detection probability.  
Figure 4: In a homogeneous environment: (**a**) target detection probability of each detector, (**b**) probability of environment type determination for VI-type detectors.  
one interference in the leading window, three interferences in the leading window, and one interference target in both windows. Performance comparisons for each detector are shown in Figure 6. Figure 6a-c show the performance comparison of each detector in different environments: one interference in the leading window, three interferences in the leading window, and one interference in both windows.  
As shown in Figure 5a-c, under low SNR conditions, VI-type detectors are prone to misjudging the type of environment, whereas under high SNR conditions, it can correctly determine the type of environment with a higher probability. As shown in Figure 6a-c, the curves, from top to bottom, represent CFVI-CFAR, KLQ-CFAR, OS-CFAR, VIHECMOS-CFAR, and CA-CFAR. The detection probability of CFVI-CFAR is significantly higher than that of VIHECMOS-CFAR. The average detection probability of detectors in different environments with SNR ranging from 0 to 35 dB is provided in Table 11.  
As shown in Table 11, compared with VIHECMOS-CFAR, the average detection probability of CFVI-CFAR increases by 3.66% in an environment with one interference in the leading window, by 4.79% in an environment with three interferences in the leading window, and by 5.94% in an environment with one interference in both windows. CFVI-CFAR utilizes two channels of collected information for fuzzy fusion and further enhances the use of decision information through composite decision-making techniques, thereby significantly improving the probability of target detection in a multiple-target environment.  
To further evaluate the detection performance of CFVI-CFAR in multiple-target environment, receiver operating characteristic (ROC) curves are used for comparison, with the  
Figure 5: The probability of environment type determination for VI-type detectors: (**a**) there is one interference in the leading reference window, (**b**) there are three interferences in the leading reference window, (**c**) there is one interference in both the leading and lagging reference window.  
Figure 6: The detection probability of various detectors: (**a**) there is one interference in the leading reference window, (**b**) there are three interferences in the leading reference window, (**c**) there is one interference in both reference windows.  
number of interferences set to 4 and SNR = 15 dB. The comparison results are shown in Figure 7.  
As shown in Figure 7, the ROC curves from top to bottom are CFVI-CFAR, KLQ-CFAR, VIHCEMOS-CFAR, OS-CFAR and CA-CFAR, respectively. Even when the constant false alarm rate changes, the CFVI-CFAR detector consistently maintains better detection performance than the other detectors.