In this paper, a novel direction of arrival (DOA) estimation algorithm is proposed for monostatic multiple-input multiple-output (MIMO) radar. Due to redundancy of the received data of monostatic MIMO radar, the computational complexity of the inversion of the covariance matrix is increased. In order to reduce the large computational complexity of existing subspace-based algorithms, we perform two reduced-dimensional operations. First, the high-dimensional received data is transformed the low-dimensional received data through a special transformation matrix. Second, the low-dimensional received data is transformed into beamspace. The receive beamspace filter is designed using convex optimization, and it can flexibly control the bandwidth and limit the sidelobe level. To ensure that the converted noise is Gaussian white noise, the prewhitening process is performed. Based on the above precondition, the reduced-dimensional beamspace technique can be effectively combined with the unitary ESPRIT model. Finally, the Cramer-Rao bound (CRB) on angle estimation in element space and reduced-dimensional beamspace is calculated for performance analysis. Numerical simulations verify the effectiveness of the proposed algorithm.

针对单基地多输入多输出（MIMO）雷达，提出了一种新颖的到达方向（DOA）估计算法。由于单基地MIMO雷达接收数据的冗余性，协方差矩阵求逆的计算复杂度增加。为了减少现有的基于子空间的算法的大量计算复杂性，我们执行了两个降维运算。首先，通过特殊的转换矩阵将高维接收数据转换为低维接收数据。其次，将低维接收数据转换为波束空间。接收波束空间滤波器采用凸优化设计，可以灵活控制带宽并限制旁瓣电平。为了确保转换后的噪声是高斯白噪声，执行预增白过程。基于上述前提，可以将降维波束空间技术与统一的ESPRIT模型有效结合。最后，计算了单元空间和降维波束空间中角度估计的Cramer-Rao边界（CRB），以进行性能分析。数值仿真验证了所提算法的有效性。