Generalized Minimum Noise Subspace For Array Processing

被引：12

作者：

Viet-Dung Nguyen ^{[1
]}

Abed-Meraim, Karim ^{[1
]}

Nguyen Linh-Trung ^{[2
]}

Weber, Rodolphe ^{[1
,3
]}

机构：

[1] Univ Orleans, PRISME Lab, F-45100 Orleans, France

[2] Vietnam Natl Univ, Univ Engn & Technol, Hanoi, Vietnam

[3] CNRS, INSU, Stn Radioastron Nancay, Observ Paris, F-18330 Nancay, France

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2017年 / 65卷 / 14期

关键词：

Batch and adaptive algorithms; principal andminor subspace; MNS; GMNS; PCA; MCA; parallel computing; radio frequency interference (RFI) mitigation; radio astronomy;

D O I：

10.1109/TSP.2017.2695457

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Based on theminimum noise subspace (MNS) method previously introduced in the context of blind channel identification, generalized minimum noise subspace (GMNS) is proposed in this paper for array processing that generalizes MNS with respect to the availability of only a fixed number of parallel computing units. Different batch and adaptive algorithms are then introduced for fast and parallel computation of signal (principal) and noise (minor) subspaces. The computational complexity of GMNS and its related estimation accuracy are investigated by simulated experiments and a real-life experiment in radio astronomy. It is shown that GMNS represents an excellent tradeoff between the computational gain and the subspace estimation accuracy, as compared to several standard subspace methods.

引用

页码：3789 / 3802

页数：14

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