Improved Adaptive Constant False Alarm Rate Detector Based on Fuzzy Theory for Multiple-Target Scenario - Xudong Yang; Chunbo Xiu (Applied Sciences)
An improved adaptive constant false alarm rate (CFAR) detector based on fuzzy theory is proposed to address the issue of poor detection performance and robustness of the variability index (VI) class CFAR detectors due to the misjudgment of the background environment and other reasons. The integration of the order statistic threshold adjustable detection algorithm (OSTA) into the adaptive CFAR detector has the potential to address the aforementioned issue. Therefore, in a clutter edge environment, the ratio of the means of the leading and lagging windows is calculated separately, and the differences between these mean ratios and predefined thresholds are used as inputs to the fuzzy inference machine, and the background clutter estimation of the OSTA is determined based on the fuzzy output, which can extend the range of values of the background clutter estimation, and improve the detection performance of the OSTA in this environment. The Kaigh–Lachenbruch quantile detection algorithm (KLQ) exhibits robust detection performance in multiple-target environments. Therefore, KLQ is used to detect targets in this environment, further improving the detection performance of the detector. The experimental results show that in multiple-target environments with an average misjudgment rate of 27.48%, the proposed detector increases the detection probability by 15.58% compared to the recently proposed variability index heterogeneous clutter estimate modified ordered statistics CFAR detector (VIHCEMOS-CFAR), and in a clutter edge environment, the false alarm rate of the proposed detector was reduced by approximately 89.64% compared to VIHCEMOS-CFAR. Additionally, the effectiveness of the proposed detector is also validated by real clutter data. It can be seen that the proposed adaptive CFAR detector has better robustness to the misjudgment of the background environment and better overall detection performance regardless of the environment.