.png){kind=icon}
.png){kind=icon}
Return
State-updating-based DOA estimation using sparse Bayesian learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apacoust.2022.108719.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recent studies of the direction-of-arrival (DOA) estimation reveal that the methods based on sparse Bayesian learning (SBL) exhibit many advantages over conventional approaches. However, these methods still face difficulties in practical applications due to their high computational complexity. To address the problem, a state-updating-based DOA estimation method using sparse Bayesian learning is proposed in this paper. In the proposed method, the state filter is employed to establish a recursive link among the source states within the observation time, and a probability distribution-based fitting technique is devel-oped to fit the initial values for the iterative process by the source states. Numerical simulations and experimental results demonstrate that, the proposed method yields significantly reduced computational complexity and improved DOA estimation accuracy.(c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Sparse Bayesian learning
Hyperparameters initialization
Direction-of-arrival
State filter