知识辅助的APR非均匀样本检测方法

Knowledge-aided APR Nonhomogeneous Samples Detection Method

针对空时自适应处理(STAP)中样本协方差矩阵受强干扰目标污染时检测性能下降的问题,提出了一种知识辅助的自适应功率剩余(KA-APR)非均匀样本检测方法。该方法将杂波先验知识与自适应功率剩余非均匀检测器(APR NHD)相结合,对训练样本进行有效选择。仿真结果表明,相对于传统的APR方法,KA-APR方法能更有效剔除存在强干扰目标的样本,提高训练样本被强干扰目标污染时空时自适应处理的检测性能。

The target detection performance decreases in space time adaptive processing （STAP） when the covariance matrix is esti- mated with secondary data contaminated by strong interference-targets. To solve the problem, a knowledge-aided adaptive power residue （KA-APR） nonhomogeneous samples detection method is proposed in this paper, which integrates the clutter prior knowl- edge into the adaptive power residue nonhomogeneity detector （APR NHD） in the training samples selection. The simulation re- sults show that the KA-APR method eliminates contaminated training samples more effectively and improves the detection perform- ance of STAP compared with traditional APR method.