中文摘要：

本文考虑了色高斯干扰条件下MIMO-STAP稳健波形优化问题以提高非完备杂波先验知识条件下多输入多输出（MIMO）雷达体制下空时自适应处理（STAP）最坏情况下探测性能。由于高斯干扰（包括杂波、干扰以及热噪声）场景下最大化系统输出信干噪比（SINR）等价于最大化MIMO-STAP检测性能,因而在本文所建立杂波协方差估计误差的模型基础上,总功率发射以及参数不确定凸集约束下,经推导可得稳健波形优化问题。为求解得到的复杂非线性问题,本文提出了一种迭代算法以优化发射波形相关阵（WCM）从而最大化凸不确定集上最差情况下的输出SINR进而改善最差情况下MIMO-STAP的检测性能。通过利用对角加载（DL）方法,所提算法中的每个迭代步骤皆可表示为能获得高效求解的半定规划（SDP）问题。与非稳健方法及非相关波形相比,数值实验验证了本文所提方法的有效性。

英文摘要：

In this paper,we address the problem of robust waveform optimization with imperfect clutter prior knowledge to improve the worst-case detection performance of multi-input multi-output（ MIMO） space-time adaptive processing（ STAP）in the presence of colored Gaussian disturbance. Due to the fact that maximization of the output signal-interference-noise-ratio（ SINR） is equivalent to maximizing the detection performance of MIMO-STAP in the case of Gaussian disturbance（ including clutter,jamming,and thermal noise）,based on the model of the estimation error of the clutter covariance matrix built in this paper,with the total transmitted power and parameter uncertainty convex set constraints,the robust waveform optimization problem can be derived. To tackle the resultant complicated and nonlinear issue,an iterative algorithm is proposed to optimize the waveform covariance matrix（ WCM） for maximizing the worst-case output SINR over the convex uncertainty set such that the worst-case detection performance of MIMO-STAP can be maximized. By exploiting the diagonal loading（ DL） method,each iteration step in the proposed algorithm can be reformulated as a semidefinite programming（ SDP） problem,which can be solved very efficiently. Numerical examples verify the effectiveness of the proposed method,as compared to the non-robust method and uncorrelated waveforms.