| Autor: |
Huafei Wang, Xianpeng Wang, Liangtian Wan, Mengxing Huang |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 6, Pp 64688-64697 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2018.2877727 |
| Popis: |
The performance of traditional sparse representation-based direction-of-arrival (DOA) estimation algorithm is substantially degraded in the presence of non-uniform noise and off-grid gap caused by the discretization processes. In this paper, a robust sparse Bayesian learning method is proposed for off-grid DOA estimation with non-uniform noise. In the proposed method, the covariance matrix of non-uniform noise is reconstructed by a modified inverse iteration method. Then, the discrete sampling grid points in the spatial domain are treated as dynamic parameters, and the expectation-maximization algorithm is used to iteratively refine the position of the discretization grid points. This refinement procedure is implemented by solving a polynomial. The simulation results indicate that the proposed method can maintain excellent DOA estimation performance with uniform or non-uniform noise. Furthermore, it can also achieve satisfactory performance under a coarse grid condition. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
