Title :
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
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;
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
DOI :
10.1109/ICCIAS.2006.295415
