Ionospheric ionogram denoising based on Robust Principal Component Analysis

Author

Lang Shinan ; Zhao Bo ; Wang Shun ; Liu Xiaojun ; Fang Guangyou

Author_Institution

Key Lab. of Electromagn. Radiat. & Sensing Technol., Inst. of Electron., Beijing, China

fYear

2012

fDate

19-20 May 2012

Firstpage

1956

Lastpage

1960

Abstract

This paper proposes a preprocess optimization analysis called Robust Principal Component Analysis (RPCA) to eliminate the noises of ionospheric ionograms. Through the theoretical analysis of the basic principle and validity of this method and simulation results, we point out the feasibility of this method and give a useful algorithm named accelerated proximal gradient method (APGp) to solve this RPCA problem. Finally, we verify the feasibility of this method by some simulation results.

Keywords

geophysical image processing; gradient methods; image denoising; ionospheric techniques; principal component analysis; RPCA problem; accelerated proximal gradient method; ionospheric ionogram denoising; ionospheric ionograms; preprocess optimization analysis; robust principal component analysis; Acceleration; Algorithm design and analysis; Noise; Optimization; Principal component analysis; Robustness; Terrestrial atmosphere; Robust Principal Component Analysis (RPCA); accelerated proximal gradient method (APGp); ionospheric ionograms;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems and Informatics (ICSAI), 2012 International Conference on

Conference_Location

Yantai

Print_ISBN

978-1-4673-0198-5

Type

conf

DOI

10.1109/ICSAI.2012.6223432

Filename

6223432