.png){kind=icon}
返回
Array processing using joint diagonalization
.png){kind=icon}
PRE
AI
DOI：10.1016/0165-1684(96)00021-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A new approach to array processing (i.e., direction-finding, signal separation and reconstruction, and calibration) is considered. The main advantages of the advocated approach is that the multidimensional search associated with maximum likelihood-based estimators or the single-dimensional search associated with MUSIC-type methods are eliminated. While the proposed method is based on the Analytical Constant Modulus Algorithm that was recently proposed by Van der Veen and Paulraj, it is not limited to constant modulus signals or any other specific type of signals. The sensor array elements are assumed to have the same, up to a multiplicative constant, angle-dependent, unknown gain pattern. We show that under this assumption it is possible to estimate the array response matrix and then use the result for direction finding, if the nominal array manifold is known, at least approximately. It is also possible to use the estimated array response matrix in order to separate and reconstruct the signals, or calibrate the array shape or response. We provide performance analysis of the algorithm and compare the results with computer simulations. We also examine the sensitivity of algorithm to the model assumptions. The method can be applied in the presence of specular multipath (using spatial smoothing) but it is not suitable for signal separation in the presence of severe diffuse multipath.
Keyword:
MAXIMUM-LIKELIHOOD
EIGENSTRUCTURE METHODS
PARAMETER-ESTIMATION
SIGNALS
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:
9.9K
被引数:
1.7W
机构
暂无机构信息
引用论文
Trap density of states in n-channel organic transistors: variable temperature characteristics and band transport.png)
AIP Advances
OAAI
AIP AdvancesIF0
OAAI分享收藏
Hydrogen-bond basicity of the sulfonyl group. The case of strongly basic sulfonamidates RSO2NNMe3
PREAI
分享收藏分享收藏
查看更多内容