Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation
Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation
复制标题
用于离网 DOA 估计的复杂多快照稀疏贝叶斯学习
DOI：
10.1155/2022/4500243
复制
发表时间：
2022-02
影响因子：
1.5
通讯作者：
谢全明
中科院分区：
文献类型：
--
作者：
刘庆华;何垣鑫;丁凯;谢全明
基于稀疏信号重构(SSR)的波达方向(DOA)估计是近年来发展起来的一种新方法。稀疏贝叶斯学习是SSR的一种典型方法。在SBL中，稀疏诱导先验模型采用了高斯混合模型中的两层递阶模型。然而，该模型主要应用于实值信号模型。为了将SBL应用于复值信号模型，提出了一类基于复高斯尺度混合(CGSM)的复值信号模型的稀疏诱导先验，并给出了几种经典先验的复数形式对应的特例，有助于分析不同建模方法之间的关系。此外，实数模型和复数模型的SBL形式的表达由参数值统一，这使得推广和改进SBL方法的性质成为可能。最后，将SBL复值形式应用到离网DOA估计复值模型中，并比较了不同稀疏诱导先验之间的性能。理论分析和仿真结果表明，该算法能够有效地处理复值信号模型，具有较低的算法复杂度。
Direction of arrival (DOA) estimation has recently been developed based on sparse signal reconstruction (SSR). Sparse Bayesian learning (SBL) is a typical method of SSR. In SBL, the two-layer hierarchical model in Gaussian scale mixtures (GSMs) has been used to model sparsity-inducing priors. However, this model is mainly applied to real-valued signal models. In order to apply SBL to complex-valued signal models, a general class of sparsity-inducing priors is proposed for complex-valued signal models by complex Gaussian scale mixtures (CGSMs), and the special cases correspond to complex versions of several classical priors are provided, which is helpful to analyze the connections with different modeling methods. In addition, the expression of the SBL form of the real- and complex-valued model is unified by parameter values, which makes it possible to generalize and improve the properties of the SBL methods. Finally, the SBL complex-valued form is applied to the offgrid DOA estimation complex-valued model, and the performance between different sparsity-inducing priors is compared. Theoretical analysis and simulation results show that the proposed algorithm can effectively process complex-valued signal models and has lower algorithm complexity.
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影响因子：
3.9
作者：
Xiaohuan Wu;Weiping Zhu;Jun Yan;Zeyun Zhang
通讯作者：
Xiaohuan Wu;Weiping Zhu;Jun Yan;Zeyun Zhang
两种基于稀疏的离网到达方向估计方法
影响因子：
4.4
作者：
Wu Xiaohuan;Zhu Wei-Ping;Yan Jun;Zhang Zeyun
通讯作者：
Zhang Zeyun
DOI：
10.1109/icassp.2014.6854226
发表时间：
2014-05
期刊：
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
--
作者：
Qisong Wu;Yimin D. Zhang;M. Amin;B. Himed
通讯作者：
Qisong Wu;Yimin D. Zhang;M. Amin;B. Himed
影响因子：
5.4
作者：
Yang, Zai;Xie, Lihua;Zhang, Cishen
通讯作者：
Zhang, Cishen
影响因子：
5.7
作者：
Oliveri, Giacomo;Carlin, Matteo;Massa, Andrea
通讯作者：
Massa, Andrea