DocumentCode :
2395113
Title :
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
Link To Document :
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