【摘要】 提出一种高斯杂波和噪声背景下的极化鲁棒自适应检测算法.因阵列校正误差、波束指向误差等因素引起的目标标称导向矢量与真实的不一致,可能会导致检测性能下降.通过在目标标称导向矢量子空间增加一些矢量,提出一种基于子空间的对模型失配鲁棒的检测算法.仿真结果表明,当目标导向矢量误差较大时,算法仍能取得较好的检测性能.更多还原

【Abstract】 A method of polarimetric robust adaptive target detection in Gaussian clutter and noise was presented.There potentially exist many reasons,such as imperfectly calibrated arrays,pointing errors,etc.,which may lead to the detection loss due to the mismatch between the presumed target steering vector and the actual one.A subspace based robust detector against model imperfection was proposed by adding some steering vectors close to the presumed one in the subspace constraint.Simulation results show that the proposed method performs well in the case of large target steering vector error.更多还原