Name
Abstract | ||
|---|---|---|
In this paper, a computation-efficient method utilizing sparse recovery technique is proposed to address the problem of direction of arrival (DOA) estimation based on sample covariance matrix vectors. In the development of the new method, the DOA estimation problem is reformulated in a way that each column of the sample covariance matrix is reintroduced as pseudo-measurements. With this reformulation, multiple candidates of the DOA estimation are obtained by utilizing sparse recovery concept in which an explicit formula of the threshold parameter is provided. The optimal DOA estimation is then selected by employing the maximum likelihood estimation criterion from these multiple candidates. The proposed approach not only has higher resolution and ability of processing coherent sources without the need of decorrelation preprocessing, but also exhibits robust performance, especially in the case of low signal-to-noise ratio and/or small number of snapshots. Numerical studies confirm the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00034-016-0339-y | CSSP |
Keywords | Field | DocType |
Array signal processing, Sparse recovery, DOA estimation, Covariance vectors | Small number,Estimation of covariance matrices,Decorrelation,Pattern recognition,Sample mean and sample covariance,Direction of arrival,Maximum likelihood,Preprocessor,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 3 | 1531-5878 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaorong Jing | 1 | 6 | 3.47 |
| Xuefeng Liu | 2 | 438 | 47.61 |
| Hongqing Liu | 3 | 0 | 1.35 |