.png){kind=icon}
返回
Adaptive Beamforming for Sparse Array Based on Semi-Definite Programming
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2878153.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An adaptive beamforming (ABF) technique for sparse receiving arrays with gain/phase uncertainties is proposed. The basic idea of the proposed method is using the compressed sensing theory to estimate directions and amplitudes of the received signals with sparse array and then obtain the covariance matrix of the signals through the estimated directions and amplitudes. However, on a discrete grid, the accuracy of directions and amplitudes estimation will degrade because of the basis mismatch and the existence of the gain/phase uncertainties. It will influence the performance of the adaptive digital beamforming. In order to eliminate the influence of the gain/phase uncertainties and the basis mismatch, we propose a semi-definite programming-total least squares (SDP-TLS) method in this paper. First, we convert the problem we want to solve into a TLS framework. Then, we develop an alternating descent algorithm to solve this problem. In the algorithm we proposed, the directions and amplitudes are estimated by semi-definite programming. The covariance matrix of the signals, which is used for ABF, is obtained by the estimated directions and amplitudes. Then, the adaptive digital beamforming algorithm is adopted to form a beam with the obtained covariance matrix.
Keyword:
Compressed sensing
adaptive digital beamforming
gain/phase uncertainties
semi-definite programming-total least squares algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏
查看更多内容