基于矩阵相似度的空时二维干扰检测方法

针对干扰目标污染训练样本引起功率非均匀,造成空时自适应处理(space-time adaptive processing,STAP)目标检测性能下降这一问题,提出一种基于矩阵相似度的STAP非均匀样本选取方法。该方法首先从受污染样本与干净样本的差异性度量角度入手,采用均值Hausdorff距离度量样本矩阵相似性,然后结合凸优化包计算不同样本的相似度,最后根据相似度的不同,实现对受污染样本的剔除。仿真结果表明,同广义内积法(generalized inner product,GIP)相比,采用均值Hausdorff矩阵相似度的挑选方法对于受小干扰强度目标污染的样本检测更加有效,避免了弱干扰目标对于协方差矩阵估计的影响,从而改善了STAP在功率非均匀环境下的目标检测性能。

Interference detecting method for space-time two-dimension based on matrix similarity

The performance of target detection decreases in space-time adaptive processing (STAP) when inhomogeneity of clutter power is produced with training samples contaminated by target liking signals. In allusion to this problem, an inhomogeneous sample selection method for STAP based on matrix similarity is proposed. Firstly, mean Hausdorff distance is adopted to measure the similarity of the sample matrix in this method through the difference measurement of contaminated training samples and uncontaminated training samples. Then the similarities of different samples are calculated with the convex optimization package. The contaminated samples are rejected on the basis of different similarities. The simulation result shows that the proposed method is more effective for contaminated samples detection because of small power target liking signals compared with the generalized inner product (GIP), which eliminates the influence of small power target liking signals on the covariance matrix estimation and improves the target detection performance for STAP in inhomogeneity of clutter power environment.