基于平行嵌套阵互协方差的二维波达角联合估计算法

Joint Two-dimensional Direction of Arrival Estimation Based on Cross Covariance Matrix of Parallel Nested Array

论文提出基于平行嵌套阵互协方差的2维(Two Dimensional,2D)波达角(Direction Of Arrival,DOA)联合估计算法。算法基于两个互相平行的嵌套阵的互协方差生成较长虚拟阵列,同时将2维DOA估计问题降维为1维DOA估计问题。在构造协方差矩阵时,利用方向矩阵范德蒙特性增加虚拟快拍数,保证了孔径的最小损失。最后算法基于酉旋转不变技术(Estimation of Signal Parameters via Rotational Invariance Technique,ESPRIT)和总体最小二乘(Total Least Squares,TLS)方法进一步降低噪声影响,并获得了自动配对的2维DOA估计。相比传统平行阵下的DOA估计算法,该算法拥有更好的DOA估计性能,能辨识更多的空间信源,对空间色噪声有更强的鲁棒性。仿真结果验证了算法的有效性。

A Cross Covariance Matrix（CCM） based Two Dimensional（2D） Direction Of Arrival（DOA） estimation algorithm for parallel nested array is proposed. A long virtual array can be achieved based on the CCM between the two parallel nested arrays, and 2D DOA estimation can be transformed to a 1D DOA estimation problem. Thereafter, virtual snapshots are increased by exploiting the Vandermonde structure of direction matrix, and the aperture loss is minimized when constructing covariance matrix from the virtual array. Finally, the proposed algorithm employs unitary Estimation of Signal Parameters via Rotational Invariance Technique（ESPRIT） and Total Least Squares（TLS） to reduce further the influence of noise and achieve automatically paired 2D DOA estimation. Compared to DOA estimation algorithms using conventional parallel array, the proposed algorithm can achieve better DOA estimation performance, identify more signals and is more robust to spatial color noise. The simulation results verify the effectiveness of the proposed algorithm.