.png){kind=icon}
.png){kind=icon}
Return
Eigenvalue-based super-resolution DOA algorithm for arbitrary arrays
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apacoust.2021.108106.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In order to solve the problem that the traditional DOA estimation algorithm has low angular resolution under low SNR and small snapshot conditions, a super-resolution DOA algorithm is proposed based on Pseudo data reconstruction. Firstly, using the odd function of the array steering vector function, a new matrix is constructed by using original array received covariance matrix and its complex conjugate. Then, the scanning source is introduced into the new matrix to construct a scanning covariance matrix. When the DOA of the scanning source is the same as the DOA of the target or the symmetric DOA of the target, the first noise eigenvalue of the scanning covariance matrix is twice as large as the first eigenvalue of the noise subspace corresponding to the original signal covariance matrix. Otherwise, there is no such relationship. In consequence, this property is used to construct the spatial spectrum for the DOA estimation. Then the interpolation method is used to overcome the shortcomings that the algorithm in this paper cannot be used for arbitrary arrays. Finally, simulation experiments verify the correctness and feasibility of the proposed algorithm. (C) 2021 Published by Elsevier Ltd.
Keywords:
DOA
Scanning source
Super-resolution
Spatial spectrum
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.