基于稀疏恢复的循环平稳信号DOA估计

DOA Estimation Method for Cyclostationary Signals Based on Signal Sparse Recovering

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 signal sparse recovering, a new direction of arrival （DOA） estimation method is pro-posed for uncorrelated cyclostationary signals. Firstly, by applying vectorization on the second order cyclic autocorrelation matrix of the array and by dividing the space of interest into several sectors to construct the overcomplete direction matrix, the sparse model for cyclostationary signal based on Khatri-Rao product is obtained. Then, by applying the convex technique to the model, the DOA values with high precision are estimated according to the poison of nonzero elements of the restored sparse signal. Compared with the conventional cyclic method, the proposed method has higher precision, and is also suitable to the scenario that the number of signals is more than that of arrays. The validity of the method is supported by simulation re-suits.