基于重构噪声子空间的相干信号DOA估计

传统DOA(direction of arrival)估计算法无法处理相干信号，因此提出一种基于重构噪声子空间的高精度DOA估计算法.该算法利用阵元接收数据的自协方差与互协方差信息构造成增广矩阵作为新的协方差矩阵，对该矩阵进行奇异值分解得到相应的噪声子空间和特征值矩阵.为了获得更精确的信号向量，重构一个由新特征值矩阵对应的特征向量所组成的噪声子空间.最后通过谱峰搜索得到DOA估计值.算法不影响对非相干信号估计的效果，并且比IMMUSIC(improved multiple signal classification)算法具有更高的估计精度，在低信噪比及信号入射间隔较小的情况下也有良好的准确性.仿真结果表明，提出的改进算法在低信噪比及低采样快拍数的条件下，能有效估计出相干信号的波达方向.

DOA Estimation of Coherent Signals Based on Reconstructed Noise Subspace

Traditional DOA(direction of arrival) estimation algorithms often fail to deal with coherent signals， so a new DOA estimation method with high accuracy based on reconstructed noise subspace is proposed. This method constructs an augmented matrix as a new covariance matrix by using the auto-covariance and cross-covariance information of uniform liner array， and then， the corresponding noise subspace and the eigen value matrix can be obtained through singular value decomposition on the augmented matrix. To obtain more accurate signal vectors， a new noise subspace can be reconstructed by the eigenvectors associated with the new eigen value matrix. Finally， DOA estimation is completed through spectral peak searching. The proposed algorithm doesnot affect the estimation effect of independent signals. Compared with the IMMUSIC(improved multiple signal classification) algorithm， the proposed algorithm has higher estimation accuracy， especially under the conditions of low signal-to-noise ratio and small signal incidence interval. The simulation results show that even for the conditions of low signal-to-noise ratio and low sampling snapshot number， the improved algorithm can effectively estimate the DOA.