.png){kind=icon}
Return
A Sparse Representation-Based DOA Estimation Algorithm With Separable Observation Model
.png){kind=icon}
PRE
AI
DOI：10.1109/LAWP.2015.2413814.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Conventional sparse representation (SR)-based direction-of-arrival (DOA) estimation algorithms suffer from high computational complexity. To be specific, a wide angular range and a large-scale array will enlarge the scale of the spatial observation matrix, which results in huge computation cost for DOA estimation. In this letter, a new efficient DOA estimation algorithm based on the separable sparse representation (SSR-DOA for short) is derived, in which a separable structure for spatial observation matrix is introduced to reduce the complexity. Besides, a dual-sparsity strategy is engaged to make the algorithm tractable. Experimental results show that high resolution performance can be obtained efficiently by the proposed algorithm.
Keywords:
Direction-of-arrival (DOA)
planar array
sparse representation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.8
Papers:
1.0W
Citations:
2.8W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
no more
no more