## 4 Discussion  
To address the performance degradation and lack of robustness in VI class CFAR detectors caused by background misjudment in complex environments, KLQVI-CFAR is proposed. This detector integrates fuzzy theory and the OSTA detection method, and a designed adaptive FOSTA detection algorithm. In FOSTA, the mean ratio of the leading and lagging windows is calculated separately, and their deviation from a preset threshold is used as input to the fuzzy inference machine. This allows for dynamic adjustment of the background clutter estimation range, effectively enhancing the adaptability and robustness of the OSTA algorithm under various environmental conditions. Moreover, to address the issue of missed detections in multiple-target environments common to traditional VI class CFAR detectors, KLQVI-CFAR introduces the KLQ detection algorithm, which improves detection accuracy in high-density multiple-target environments. By integrating the FOSTA and KLQ algorithms, KLQVI-CFAR demonstrates excellent target detection capability and false alarm control across different complex environments. Simulation results show that, under various typical non-homogeneous environments, KLQVI-CFAR outperforms detection algorithms such as VIHCEMOS-CFAR and KLQ-CFAR in both detection probability and false alarm probability, effectively reducing the impact of background misjudment on detection performance. Furthermore, its effectiveness and robustness in practical applications are validated through experiments on real clutter data. Although the KLQVI-CFAR detector performs excellently in various complex environments, its performance in extremely low SNR conditions still needs to be further improved. Additionally, its robustness may be affected in extreme clutter environments. This phenomenon is consistent with the observations of CFAR performance degradation in complex scenarios in recent literature. Future work will therefore concentrate on enhancing the algorithm's ability to adapt to harsh environments. This will involve enhancing computational efficiency, streamlining the algorithm's structure, and investigating the integration of lightweight deep learning mechanisms into the detection process to raise its level of intelligence and real-time.  
Figure 11: The detection probabilities of various detectors in multiple-target environments based on McMaster IPIX radar clutter data.  
**Author Contributions:** Conceptualization, C.X.; methodology, C.X. and X.Y.; software, X.Y.; validation, C.X. and X.Y.; formal analysis, X.Y.; investigation, C.X.; resources, C.X.; data curation, X.Y.; writing--original draft preparation, X.Y.; writing--review and editing, C.X.; visualization, X.Y.; supervision, C.X.; project administration, C.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.