This research stresses the problem of the two-dimensional angle estimation in monostic cross multiple-input multiple-output (MIMO)radar.Parameters estimation accuracy could be improved with the Vandermonde-like structure in the source matrix,which is always ignored by existing estimation algorithms. With the centrosymmetric of the uniform linear array (ULA)and the Vandermonde-like structure of the data model,an improved trilinear decomposition algorithm is proposed for the two-dimensional angle estimation.The unitary transform is used to construct an expand data matrix,and the two-dimensional angle estimation is then linked to the trilinear model.Due to the expand output,the virtual aperture of the array is increased,hence the pro-posed trilinear algorithm performs better than the traditional trilinear algorithm.In addition,the proposed algorithm requires neither peak searching nor eigenvalue decomposition.Furthermore,the proposed algorithm could achieve auto-matic pairing of the two-dimensional angle.Simulation results verify the effectiveness of the proposed algorithm.%研究单基地十字阵多输入多输出（multiple-input multiple-output，MIMO）雷达中目标二维角度参数估计的问题。已有的算法往往忽略了信源矩阵中的类 Vandermonde 结构，而这种特殊的结构可以提升参数估计精度。基于均匀线形阵列（uniform linear array，ULA）的中心对称特性和目标参数矩阵中的类 Vandermonde 结构，提出一种基于改进的三线性分解的二维角度估计算法。首先利用酉变换的方法构造阵列增广输出矩阵，再将二维角度估计与三线性模型相联系。由于增广输出使得阵列的虚拟孔径增大，因而本文所提算法的参数估计精度要优于传统三线性估计算法。此外，本文提及的改进算法不需进行谱峰搜索及奇异值分解，并且能对估计的二维目标角度自动配对，最后的仿真结果验证了本文算法的有效性。
展开▼
