一种多目标环境下的SAR图像自适应CFAR检测方法

摘要

Most CFAR detectors need prior information on interferingtargets in a multi-target environment, and hence can not keep stable detection performance when the detection environment changes. A new adaptive constant false alarm rate (CFAR) detector, referred as stepwise cumulation CA (SCCA) CFAR detector, is presented for target detection in a multitarget environment for SAR imagery. By employing a cell-to-cell criterion for accepting or rejecting reference samples according to an adaptive threshold, that is, clutter power estimation and standard variance which are independent of interfering targets, the samples from interfering targets are censored and the clutter samples are accumulated stepwisely.The final estimate of the noise level in the cell under test is formed using cell-averaging method with the accumulated homogeneous clutter samples. The detection performance and runtime of SCCA-statistics-based two-parameter CFAR detector are tested and compared with CA-CFAR and OS-CFAR by simulation, and the result shows the proposed detector increases the detection probability, and its runtime approximates that of OS-CFAR.%多数CFAR检测器在多目标检测环境下需要关于干扰目标的先验信息,当检测环境发生变化时,这些检测器很难维持稳定的检测性能.针对多目标环境下的SAR图像目标检测,提出一种新的自适应CFAR(恒虚警)检测器.该检测器利用局部的杂波功率水平估计以及目标和杂波的方差特征筛选出参考窗中的均匀杂波像素,同时剔除掉干扰目标像素;在筛选过程中,每一步使用的判决门限根据上一步的判决结果自动更新;最后对筛选出的样本点作单元平均处理形成检验统计量;完全不需要干扰目标的任何先验信息.利用实测数据仿真研究了该检测器的检测性能与运行效率,实验结果表明,相对单元平均CFAR检测器及有序统计量CFAR检测器,该检测器提高了检测性能,保留了目标精细的结构特征,而运行效率与有序统计量CFAR检测器相当,很具实用性.