DocumentCode
1844812
Title
General method for empirical data decomposition filtering design
Author
Wu Xiaoqin ; Guo Zhen ; Zhang Hongke
Author_Institution
Electr. Inf. Eng. Coll., Beijing Jiaotong Univ., Beijing, China
Volume
1
fYear
2012
fDate
21-25 Oct. 2012
Firstpage
732
Lastpage
736
Abstract
Based on the research on Empirical Data Decomposition (EDD), the structure for Empirical Data Decomposition is proposed in which the high pass filter is composed of a predictor and an adder. In terms of reconstructing requirement, the filter design rule is presented when the EDD analysis filter and synthesis filter are restricted as FIR filter. The relationship between equivalent synthesis filter and analysis filter is also presented. Finally the structure for the synthesis filter is discussed. Except for the FIR requirement, there is no additional restriction for the predictor, so the filter can be easily designed to satisfy different requirements. EDD is suitable not only for stationary data analysis or piece-wise stationary data analysis but also for non-stationary data analysis.
Keywords
FIR filters; adders; decomposition; high-pass filters; prediction theory; signal reconstruction; signal synthesis; EDD; adder; empirical data decomposition filtering design; equivalent FIR filter synthesis; high pass filter; nonstationary data analysis; piecewise stationary data analysis; predictor; signal reconstruction; EDD; FIR; Non-stationary Data Analysis; filter design;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing (ICSP), 2012 IEEE 11th International Conference on
Conference_Location
Beijing
ISSN
2164-5221
Print_ISBN
978-1-4673-2196-9
Type
conf
DOI
10.1109/ICoSP.2012.6491591
Filename
6491591
Link To Document