.png){kind=icon}
返回
Robust Variational Bayesian Inference for Direction-of-Arrival Estimation With Sparse Array
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2022.3173418.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array imperfections and outliers, making it challenging to realize accurate estimates in real applications. Facing the challenge, we propose a robust variational Bayesian inference based DOA estimation algorithm using the linear sparse array in this paper, where accurate DOA estimation with increased number of degrees of freedom (DOFs) is realized. With the mixture of von Mises model, a prior-grid scheme is further proposed to alleviate the computational burden introduced by the Bayesian variational inference framework. Since there is no restriction on the prior knowledge of the number of sources, the proposed algorithm is friendly to actual scenarios. Simulation results demonstrate the effectiveness of the proposed algorithm.
Keyword:
Direction-of-arrival estimation
Estimation
Sensor arrays
Sensors
Bayes methods
Inference algorithms
Sparse matrices
Direction-of-arrival estimation
gain-phase error
outlier
sparse array
variational Bayesian inference
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
.png){kind=icon}
Coupled Coarray Tensor CPD for DOA Estimation With Coprime L-Shaped ArrayCo-array张量CPD与co-rime l形阵列的DOA估计
PREAI
分享收藏
Novel Low-Complexity Estimators for the Shape Parameter of the Generalized Gaussian Distribution
PREAI
分享收藏
查看更多内容