Sparse representation of complex valued signals

Author

He, Zhaoshui ; Xie, Shengli ; Fu, Yuli

Author_Institution

Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou

Volume

2

fYear

2006

fDate

3-6 Nov. 2006

Firstpage

1008

Lastpage

1011

Abstract

Sparse representation of complex valued signals is addressed in this paper. Considering the statistical dependence between real part and imaginary part of a complex valued signal (e.g., the discrete-time Fourier transform of a real valued signal), a special probability density function (PDF) is introduced to describe the complex random variable in this paper. Based on this PDF, a complex sparse representation method is proposed and the corresponding algorithm is established. The experiments demonstrate the good performance of the proposed algorithm

Keywords

probability; random processes; signal representation; statistical analysis; complex random variable; complex valued signal; probability density function; sparse signal representation; statistical dependence; Biological neural networks; Biomedical engineering; Biomedical signal processing; Machine learning; Probability density function; Random variables; Signal processing; Signal processing algorithms; Sparse matrices; Strontium;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2006 International Conference on

Conference_Location

Guangzhou

Print_ISBN

1-4244-0605-6

Electronic_ISBN

1-4244-0605-6

Type

conf

DOI

10.1109/ICCIAS.2006.295415

Filename

4076111