DOI码：10.1109/LSP.2016.2636319

项目来源：National Natural Science Foundation of China (NSFC) under Project 61571211, and in part by the Open

关键字：Direction-of-arrival (DOA), polynomial root, sparse Bayesian learning (SBL), sparse representation.

摘要：The performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, which adopts a coarse grid and considers the sampled locations in the coarse grid as the adjustable parameters. We utilize an expectation–maximization algorithm to iteratively refine this coarse grid and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced, and the modeling error can be almost eliminated.

合写作者：鲍煦,徐维超,常春起

第一作者：戴继生

论文类型：期刊论文

卷号：24

期号：1

页面范围：46-50

是否译文：否

发表时间：2016-12-07

收录刊物：SCI