互相关矩阵重排获取信号子空间的二维DOA估计算法

本文提出一种L型阵列的二维子空间DOA估计算法，该算法通过重排阵列接收数据的互相关矩阵获取信号子空间，然后根据信号子空间生成一个二维谱函数，最后通过二维搜索估计信号的来波方向。由于该算法采用二维谱峰搜索，所以不需要对俯仰角和方位角进行配对。与二维MUSIC算法相比，该算法的估计精度略有下降，但该算法不需要对矩阵做特征值分解，计算量降低且易于实现。文中给出了该算法的推导过程和具体实现步骤，并进行了实验仿真，仿真结果说明了算法的有效性。 

Subspace Algorithm for 2-D DOA Estimation by Rearranging the Elements of Cross-correlation Matrix

This paper presents a subspace algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation with L-shaped array. In the proposed algorithm, we get the signal subspace by rearranging the elements of the cross-correlation matrix, then we generate a spectral function based on the signal subspace, and last we estimate the DOAs of sources by the 2-D spectral peak search. Since we utilize spectral peak search to estimate the DOAs of sources our method does not need the extra procedure to pair the 2-D angles of the sources. Comparing with the 2-D music algorithm, the estimation accuracy of the algorithm declines a bit, but our method does not need to perform eigenvalue decomposition, so the computational burden of our method is reduced and it is easy to be implemented. The derivation of the algorithm and detailed procedure are given in this paper. Simulations are presented to demonstrate the effectiveness of the proposed algorithm.