Paper Publications

DOI number:10.1109/LSP.2016.2636319

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

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

Abstract: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.

Co-author:Xu Bao,徐维超,Chunqi Chang

First Author:Jisheng Dai

Indexed by:Journal paper

Volume:24

Issue:1

Page Number:46-50

Translation or Not:no

Date of Publication:2016-12-07

Included Journals:SCI