基于L型和差嵌套阵的二维波达方向估计

针对传统L型均匀阵列二维波达方向（Direction of Arrival，DOA）估计中可估计信源数目受限于阵元数、分辨率低等问题，提出了一种新的L型和差嵌套阵列结构。该L型阵列的两个子阵布置相同，是非均匀的稀疏阵，通过阵元位置之间的差分、求和操作达到虚拟扩展阵元数目的效果，从而提升阵列的自由度。采用该阵列进行二维DOA估计时，两个子阵分别先进行一维的DOA估计，再采用PSCM（Pair-matching Signal Covariance Matrices）算法进行一维角度配对。每个子阵进行一维波达方向估计时，先采用VCAM（Vectorized Conjugate Augmented MUSIC）算法生成非均匀稀疏阵的求和求差协方差矩阵，再采用矩阵重构的方法恢复协方差矩阵的秩，最后对协方差矩阵采用MUSIC（Multiple Signal Classification）算法进行DOA估计。实验仿真表明，本阵列有着更高的自由度和估计精度。 

Two-Dimensional DOA Estimation Based on L-shaped Sum Difference Nested Array

In order to solve the problems that the number of estimable sources is limited by the number of elements and low resolution in the estimation of the direction of arrival （DOA） of the traditional L-shaped uniform array， this paper proposes a new L-shaped sum difference nested array structure. The two sub-arrays of the L-shaped array have the same layout， which is a non-uniform sparse array. The effect of virtual expansion of the number of array elements is achieved through the difference and sum operation between the positions of the array elements， which improves the freedom of the array. When using this array for two-dimensional DOA estimation， the two sub-arrays first perform one-dimensional DOA estimation， and then use the PSCM （Pair-matching Signal Covariance Matrices） algorithm for one-dimensional angle pairing. When the one-dimensional direction of arrival estimation of each sub-array is performed. Firstly， the VCAM （Vectorized Conjugate Augmented MUSIC） algorithm is used to generate the summation and difference covariance matrix of the non-uniform sparse matrix. Second， the matrix reconstruction method is used to restore the rank of the covariance matrix. Finally， the MUSIC （Multiple Signal Classification） algorithm is used to estimate the DOA of the covariance matrix. Experimental simulation shows that this array has higher degrees of freedom and estimation accuracy.