Grid Evolution Method for DOA Estimation

Off-grid direction of arrival (OGDOA) estimation methods deal with the situations where true direction of arrivals (DOAs) are not on the discretized sampling grid. However, existing OGDOA estimation methods are faced with a tradeoff between density of initial grid and computational workload. Further...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on signal processing 2018-05, Vol.66 (9), p.2374-2383
Main Authors: Wang, Qianli, Zhao, Zhiqin, Chen, Zhuming, Nie, Zaiping
Format: Article
Language:English
Subjects:
Bayes methods
Computational modeling
Direction-of-arrival (DOA)
Direction-of-arrival estimation
Estimation
grid evolution
Iterative methods
Mathematical model
off-grid
sparse Bayesian learning (SBL)
Spatial resolution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Off-grid direction of arrival (OGDOA) estimation methods deal with the situations where true direction of arrivals (DOAs) are not on the discretized sampling grid. However, existing OGDOA estimation methods are faced with a tradeoff between density of initial grid and computational workload. Furthermore, these methods fail if more than one true DOA is located in a same grid interval. In order to speed up the OGDOA estimation methods and solve the problem of more than one DOA in one initial grid interval, a grid evolution direction of arrival (GEDOA) estimation method is proposed. Based on the combination of OGDOA estimation methods and grid refinement, this new approach makes the grid nonuniformly evolve from coarse to dense. The proposed method contains two subprocesses, i.e., learning process and fission process. The learning process aims to update locations of grid points and estimate DOAs. The fission process aims to generate new grid points and guarantees that there is only one DOA in a grid interval. The two processes iterate alternatively. Finally, an adaptive and nonuniform grid and an estimated spatial spectrum based on this grid are achieved. Compared with the previous methods, GEDOA has better computational efficiency because fewer grid points are used at each iteration. Furthermore, GEDOA has better resolution and lower MSE at relative high SNR. This is because that the evolved grid obtained by this new approach is information adaptive. Numerical simulations validate the effectiveness of the proposed method.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2018.2814998