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 estimated 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 knowledge into the adaptive power residue nonhomogeneity detector (APR NHD) in the training samples selection. The simulation results show that the KA-APR method eliminates contaminated training samples more effectively and improves the detection performance of STAP compared with traditional APR method.