论文部分内容阅读
基于信号稀疏恢复的思想,提出了一种新的循环非相关平稳信号DOA估计算法。首先,对阵列的二阶循环互相关矩阵矢量化,并将感兴趣的空间划分成若干段以构造过完备的方向矩阵,从而得到基于Khatri-Rao积的稀疏模型;其次,利用凸优化技术对稀疏模型进行优化求解,并根据恢复得到的稀疏信号中非零元素的位置估计出高精度的DOA值。与传统的循环互相关算法比较,本文算法具有更高的DOA估计精度,同时也适用于信号个数多于阵元个数的场合。理论分析和仿真实验结果都验证了算法的有效性。
Based on the idea of sparse signal recovery, a new DOA estimation algorithm for non-correlated stationary signals is proposed. Firstly, the vector of the second-order cyclic cross-correlation matrix of the array is vectorized, and the space of interest is divided into several sections to construct an over-complete directional matrix to obtain a sparse model based on the Khatri-Rao product. Secondly, by using the convex optimization technique The sparse model is solved optimally, and a high precision DOA value is estimated based on the recovered non-zero elements in the sparse signal. Compared with the traditional cyclic cross-correlation algorithm, the proposed algorithm has higher DOA estimation accuracy and is also suitable for applications where the number of signals is more than the number of elements. Theoretical analysis and simulation results verify the effectiveness of the algorithm.