空时二维自适应处理的目标污染样本挑选方法

在空时二维自适应处理(STAP)中,杂波样本数据不满足独立同分布(iid)条件是影响STAP处理的一个重要因素.这种非iid特性不仅包含地面的不均匀性和散射体的内部运动,也包括了目标和一些类似目标的干扰造成的样本数据污染.在某些情况下,被目标污染的样本会对权系数矢量的估计造成明显的影响,使输出信杂比大大下降.本文首先分析了目标信号对广义旁瓣相消结构(GSC)处理方式的影响,指出采用阻塞矩阵和不包含检测单元的辅助数据不能有效消除目标信号的影响.而现有的非均匀检测(NHD)方法不能有效挑出污染样本.据此,本文提出杂波剩余功率下降的目标污染样本选择准则和两种新的非均匀样本检测方法,可以将对自适应有影响的污染样本数据剔除.试验结果证实了新方法的有效性,而且其中一种方法的运算量很小,便于工程实现,具有较高的应用价值.

The Contaminated Sample Selecting Method for STAP

The performance of STAP degrades severely when the training samples are not independently and identically distributed(iid).The iid assumption is violated not only by the nonhomogeneity of the ground and the inner moving of the scatterer but also by the targets and other interferences.In some cases,the sample contaminated by a target will affect the estimation of the weight vector effectively and the output SCNR of STAP degrades evidently.In this paper,the target effect on the generalized sidelobe cancellor(GSC) is analysed.It seems that the blocking matrix and the secondary data training can not avoid the effect effectively.And the existing nonhomogeneity detectors can not select the contaminated sample exactly also.So a new NHD criterion and two new NHD methods are proposed.The computer simulation results show good performance can be obtained.Moreover,one of them is computationally easy and suits applications.