基于有序统计和自动删除平均的恒虚警检测器

为了增强检测器对干扰的鲁棒性,基于有序统计(OS)方法和自动删除单元平均(ACCA)方法提出一种新的恒虚警检测器(MOSAC),其前沿和后沿滑窗分别采用OS和ACCA产生两个局部估计,然后取二者的和作为背景功率水平估计,从而设置自适应检测门限。在Swerling Ⅱ型目标假设下,推导出MOSAC在均匀背景下虚警概率Pfa的解析表达式,并与其它现有方案进行了比较。仿真结果表明MOSAC在均匀背景及多目标和杂波边缘引起的非均匀背景中,均具有较好的检测性能。在杂波边缘引起的非均匀背景中,虚警尖峰比MOSCM减少了一个数量级,并且样本排序时间只有OS和ACCA的1/2。

CFAR detector based on ordered statistics and automatic censoring cell averaging

In order to make the detector perform robustly against interfere background, a new CFAR detector (MOSAC-CFAR) based on ordered statistics(OS) and automatic censoring cell averaging(ACCA) is proposed. It takes the sum of OS and ACCA local estimation as a noise power estimation. Under Swerling II assumption, the analytic expression of Pfa in homogeneous background is derived. By comparison with other!schemes, the simulation results show that the detection performance of MOSAC is good both in homogeneous environment and in nonhomogeneous environment caused by strong interfering targets and clutter edges, particularly in clutter edges situation, the spike of false alarm rate of MOSAC decreases an order of magnitude than that of MOSCM, while the sample sorting time is only half that of OS and ACCA.