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本文基于自动删除单元平均(ACCA)方法和单元平均(CA)方法,提出了一种新的恒虚警检测器(ACGCA-CFAR)以提高CFAR检测的抗干扰性能。它的前沿和后沿滑窗分别采用ACCA和CA方法产生两个局部估计,然后取最大值作为背景噪声功率水平估计。在SwerlingII型目标假设下,推导出ACGCA在均匀背景下虚警概率Pfa的解析表达式,并与现有方案进行了比较,仿真和试验数据处理结果表明:ACGCA-CFAR在均匀背景和非均匀背景下均具有相当好的检测性能,而它的样本排序时间只有OS和ACCA的1/4。
Based on ACCA and CA, this paper proposes a new ACGCA-CFAR to improve the anti-jamming performance of CFAR detection. Its front and back sliding windows use ACCA and CA methods to generate two local estimates, respectively, and then take the maximum value as the background noise power level estimation. Under the Swerling II target hypothesis, the analytical expression of the false alarm probability Pfa of ACGCA in a homogeneous background is deduced and compared with the existing schemes. The simulation and experimental data processing results show that the ACGCA-CFAR has good performance in both homogeneous background and non-uniform background Under the detection performance is very good, and its sample sequencing time only OS and ACCA 1/4.