针对单元平均恒虚警检测器(CA-CFAR)在多目标干扰和杂波边缘效应下的性能局限，提出了基于子参考窗口中最小选择单元的恒定虚警检测器(MCA-CFAR)。通过选择子参考单元中的最小单元格，显著提升了检测器的性能，并在瑞利分布背景下详细推导了检测概率、虚警率和检测阈值。为进一步优化性能,设计了模糊逻辑融合检测器(FUMCACFAR)，它利用两个传感器计算空间隶属函数值，并通过代数和、代数积、MAX、MIN 四种规则进行融合，实现了平滑输出,减少了目标信息的丢失。仿真实验表明,基于代数和融合的FUMCA-CFAR 检测器在均匀和非均匀背景下均展现出优异的检测性能和抗干扰能力。

Aiming at the performance limitations of the cell-averaged constant false alarm detector (CA-CFAR) in the presence of multi-target interference and clutter fringe effects, a constant false alarm detector (MCA-CFAR) based on the minimum selection cell in the sub-reference window is proposed. The performance of the detector is significantly improved by selecting the smallest cell in the sub-reference cell, and the detection probability, false alarm rate and detection threshold are derived in detail in the context of Rayleigh distribution. To further optimize the performance, a fuzzy logic fusion detector (FUMCA-CFAR) is designed, which uses two sensors to compute the spatial affiliation function values and fuses them by four rules, namely, algebraic sum, algebraic product, MAX, and MIN, to achieve a smooth output and reduce the loss of target information. Simulation experiments show that the FUMCA-CFAR detector based on algebraic sum fusion exhibits excellent detection performance and anti-jamming ability in both uniform and non-uniform backgrounds.

TN951