Direction of Arrival Estimation for Complex Sources Through L1 Norm Sparse Bayesian Learning
Direction of Arrival Estimation for Complex Sources Through L1 Norm Sparse Bayesian Learning
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DOI：
10.1109/lsp.2019.2905164
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发表时间：
2019-05-01
影响因子：
3.9
通讯作者：
Janaswamy, Ramakrishna
中科院分区：
文献类型：
--
作者：
Bai, Hua;Duarte, Marco F.;Janaswamy, Ramakrishna
在这封信中，使用拉普拉斯分布来建模源到达方向（DoA）的先验。为了合并接收信号的真实的和虚部，我们提出了一种方法，成对估计超参数的信号系数的一部分。此外，我们提出了一个多任务算法，以扩展我们的方法的应用程序的情况下，多个measures可用。非均匀线阵的实验结果表明，该方法与现有的DOA估计方法相比，具有更高的估计效率和更高的估计精度。
In this letter, Laplace distribution is used to model the prior for the direction of arrival (DoA) of sources. In order to incorporate the real and imaginary part of the received signal, we propose a method that pairwise estimates the hyperparameters for parts of the signal coefficients. In addition, we propose a multitask algorithm to extend the application of our method to the situation where multiplemeasurements are available. Nonuniform linear arrays are used to demonstrate the validity and advantages of the proposed method including its improved efficiency and accuracy compared with the state-of-art DoA estimation methods.