Title
An Efficient Real-Valued Sparse Bayesian Learning For Non-Circular Signal'S Doa Estimation In The Presence Of Impulsive Noise
Abstract
Currently, sparse Bayesian learning (SBL) has been introduced to solve direction of arrival (DOA) estimation in different situations. In the line of DOA estimation under impulsive noise, existing SBL-based methods need large computation which will restrict their practicabilities. To address this problem, we propose an efficient method based on a real-valued SBL for non-circular signals in this paper. Firstly, received signal model is transformed into a real-valued form using the characteristic of non-circular signals' structure. Then, a sparse representation of the modified signal model is constructed in the presence of impulsive noise. Finally, SBL is applied to reconstruct the real-valued sparse model and solve the DOAs estimation. A series of simulations are carried out in different conditions to evaluate the proposed method. Simulation results demonstrate that our method shows better performance than existing methods. (C) 2020 Elsevier Inc. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.dsp.2020.102838
DIGITAL SIGNAL PROCESSING
Keywords
DocType
Volume
DOA estimation, Impulsive noise, Sparse Bayesian learning, Real-valued, Non-circular signal
Journal
106
ISSN
Citations 
PageRank 
1051-2004
1
0.35
References 
Authors
0
3
Name
Order
Citations
PageRank
Jiacheng Zhang1256.25
Tianshuang Qiu231343.84
Shengyang Luan364.85