Fast communication: Off-grid DOA estimation using array covariance matrix and block-sparse Bayesian learning
Fast communication: Off-grid DOA estimation using array covariance matrix and block-sparse Bayesian learning
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DOI：
10.1016/j.sigpro.2013.11.022
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发表时间：
2014-05
影响因子：
4.4
通讯作者：
Yi Zhang;Z. Ye;Xu Xu-Xu;Nan Hu
中科院分区：
文献类型：
--
作者：
Yi Zhang;Z. Ye;Xu Xu-Xu;Nan Hu
提出了一种基于一种新模型的离网波达方向估计方法。该模型基于样本协方差矩阵和导引向量的离网表示。在该模型的基础上，假设其等效信号满足独立的高斯分布，其噪声方差可以归一化为1。利用块稀疏贝叶斯算法估计离网DOA。该方法的优点是考虑了等效信号样本矩阵的每一行都存在时间相关性，且不需要估计归一化噪声方差。此外，该算法可以在不知道信号个数的情况下工作。数值仿真结果表明，该方法具有较好的性能。
A new method based on a novel model for off-grid direction-of-arrival (DOA) estimation is presented. The novel model is based on the sample covariance matrix and the off-grid representation of the steering vector. Based on this model, its equivalent signals are assumed to satisfy independent Gaussian distribution and its noise variance can be normalized to 1. The off-grid DOAs are estimated by the block sparse Bayesian algorithm. The advantages of the proposed method are that it considers the temporal correlation existed in each row of the equivalent signal sample matrix and the normalized noise variance does not need to be estimated. Moreover, this algorithm can work without the knowledge of the number of signals. Numerical simulations demonstrate the superior performance of the proposed method.