# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 1 Introduction  
Constant false alarm rate (CFAR) target detection technology adaptively adjusts the detection threshold based on variations in clutter levels, aiming to maximize the detection probability while maintaining a constant false alarm rate [1]. The CFAR detection technology has been widely applied in numerous fields such as autonomous driving and ship detection [2, 3]. Classic CFAR detectors include cell-averaging CFAR (CA-CFAR), greatest of CFAR (GO-CFAR), smallest of CFAR (SO-CFAR), order statistics of CFAR (OS-CFAR), among others [4]. The background clutter estimation of the CA-CFAR detector is based on the mean value of reference cells in the leading and the lagging reference windows [5]. CA-CFAR has excellent detection performance in homogeneous backgrounds. However, it has poor false alarm control ability in a clutter edge environment and faces challenges in adapting to a multiple-target environment. Therefore, GO-CFAR and SO-CFAR are proposed in Ref. [6]. In GO-CFAR, the background clutter estimation is obtained by the larger sum of the reference cells of the leading and the lagging reference windows. GO-CFAR has excellent false alarm control ability in clutter edge environment, but it haspoor detection performance in multiple-target environment. In the SO-CFAR detector, the background clutter estimation is based on the smaller sum of the reference cells of the leading and the lagging reference windows [7]. SO-CFAR improves the detection performance in the environment where there are interferences in a single reference window. However, SO-CFAR has poor false alarm control ability in a clutter edge environment, and its detection performance deteriorates when interferences exist on both the leading and the lagging reference windows. In order to improve the detection performance in a multiple-target environment, the OS-CFAR detector is proposed [8]. The \(k\)th reference cell in ascending order is determined as the background clutter estimation by the sample quartile estimator. OS-CFAR improves detection performance in a multiple-target environment, but its performance degrades in a homogeneous environment and a clutter edge environment. To this end, the Kaigh-Lachenbruch Quantile constant false alarm rate (KLQ-CFAR) detector is proposed as an improvement to OS-CFAR [9]. In KLQ-CFAR, the sample quartile estimator is replaced by the Kaigh-Lachenbruch Quantile estimator, resulting in improved detection performance in homogeneous environment and multiple-target environment. Overall, the detectors mentioned above are usually typically suitable for the detection requirements of a single environment and lack adaptability to the changing clutter environment. For this, the variability index CFAR (VI-CFAR) detector is proposed in Refs. [10; 11]. The VI-CFAR detector combines the advantages of three detection methods: CA-CFAR, GO-CFAR, and SO-CFAR. It dynamically selects the estimation method for clutter power level based on the variability index (VI)and the mean ratio (MR) and adapts to the detection requirements of different environments. However, VI-CFAR suffers from the issue of environmental misjudgment, which leads to a degradation in detection performance. This issue can be addressed by refining or replacing the detection methods used in VI-CFAR. Modified variability index CFAR is proposed in Ref. [12]. In the modified variability index CFAR, the SO-CFAR detection method in VI-CFAR is replaced by the censored mean level detector CFAR, which enhances detection performance in a multiple-target environment, but exhibits the false alarm control ability in clutter edge environment. VIHCEMOS-CFAR detector is proposed in Ref. [13]. In VIHCEMOS-CFAR, GO-CFAR detection method in VI-CFAR is replaced by the heterogeneous clutter estimate CFAR detection method, and SO-CFAR is replaced by the modifiable ordered statistical CFAR in a multiple-target environment.