.png){kind=icon}
返回
Sparse Bayesian Learning Using Generalized Double Pareto Prior for DOA Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2021.3104503.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, we propose a novel sparse Bayesian learning (SBL) algorithm using Generalized Double Pareto (GDP) prior to enhance the performance of direction of arrival (DOA) estimation for complex signals. Firstly, a novel hierarchical prior model is formulated for complex signals so that the marginal distribution of the complex signal is the GDP distribution, which promotes the sparsity more significantly than conventional priors used in SBL. Secondly, a novel fixed-point update rule of the hyperparameters is derived to speed up the convergence of the proposed SBL. Finally, a refined DOA searching method is also derived to tackle the grid-mismatch problem. Simulation results demonstrate the improved accuracy and efficiency of the proposed algorithm in low SNR and limited snapshots scenarios compared with other SBL-based DOA estimation methods.
Keyword:
Economic indicators
Estimation
Direction-of-arrival estimation
Bayes methods
Convergence
Signal processing algorithms
Sensor arrays
Direction of arrival
generalized double Pareto prior
complex signals
sparse Bayesian learning
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Kinetic and Equilibrium Studies on the Adsorption of Crystal Violet Dye using Kaolin as an Adsorbent
PREAI
分享收藏分享收藏分享收藏分享收藏
查看更多内容