全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
Mathematics  2014 

Polynomial Fourier Domain as a Domain of Signal Sparsity

Full-Text   Cite this paper   Add to My Lib

Abstract:

A compressive sensing (CS) reconstruction method for polynomial phase signals is proposed in this paper. It relies on the Polynomial Fourier transform, which is used to establish a relationship between the observation and sparsity domain. Polynomial phase signals are not sparse in commonly used domains such as Fourier or wavelet domain. Therefore, for polynomial phase signals standard CS algorithms applied in these transformation domains cannot provide satisfactory results. In that sense, the Polynomial Fourier transform is used to ensure sparsity. The proposed approach is generalized using time-frequency representations obtained by the Local Polynomial Fourier transform (LPFT). In particular, the first-order LPFT can produce linear time-frequency representation for chirps. It provides revealing signal local behavior, which leads to sparse representation. The theory is illustrated on examples.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133