协方差矩阵类算法在MIMO雷达大失配误差情形下依然具有很强的鲁棒性，但往往存在计算复杂度较高的问题。针对高性能与低计算复杂度间的矛盾，提出对失配误差模型进行修正，从两方面改进算法：一方面基于双边约束的方式，在发射端和接收端分别建立单边导向矢量误差集，然后获取离散的联合导向矢量失配误差集；另一方面基于小不确定集模型结构特点，提出用K-means聚类算法获取少量加权特征采样点来代替原先大量的离散采样点，减少协方差矩阵重构所需采样点数，从而降低计算复杂度。仿真实验表明，所提算法在失配误差较大情形下具有很强的鲁棒性，且输出SINR性能达到最优。

The covariance matrix algorithms have strong robustness under the condition of large mismatch errors of MIMO radar.Howeverthose algorithms usually have high computational complexity.In view of the contradiction between high performance and low computational complexity, this paper proposes a method to modify the mismatch error model. The model is modified from two aspects. Firstly, based on bilateral constraining, the error set of the unilateral steering vector is set up to constraint the errors of the transmitter and the receiverand the mismatch error set of the joint steering vector was obtained. Secondly, based on the structural characteristics of the uncertainty set model, K-means clustering algorithm is used to obtain a small number of weighted characteristic sampling points to replace the previous large number of discrete sampling points, so as to reduce the number of sampling points needed for covariance matrix construction and thus reduce computational complexity. Simulation results show that the proposed algorithm has strong robustness under the condition of large mismatch errors and presents outstanding performance in improving the output SINR.