PRINCIPAL COMPONENT ANALYSIS FOR NONCIRCULAR SIGNALS IN THE PRESENCE OF CIRCULAR WHITE GAUSSIAN NOISE

被引：0

作者：

Li, Xi-Lin ^{[1
]}

Anderson, Matthew ^{[1
]}

Adali, Tuelay ^{[1
]}

机构：

[1] Univ Maryland Baltimore Cty, Baltimore, MD 21250 USA

来源：

2010 CONFERENCE RECORD OF THE FORTY FOURTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS (ASILOMAR) | 2010年

关键词：

D O I：

暂无

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：

The commonly used principal component analysis (PCA) assumes circular Gaussian distribution for the observed complex random variables. This paper extends PCA to the general case where the signals can be noncircular, and introduces a new PCA method called the noncircular PCA (ncPCA). We study the properties of ncPCA and propose an efficient algorithm for its implementation. Numerical results are presented to demonstrate its advantages in signal detection and subspace estimation, in particular when the circularity assumptions on data do not hold.

引用

页码：1796 / 1801

页数：6

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