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
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;
Conference_Titel :
Signal Processing (ICSP), 2012 IEEE 11th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-2196-9
DOI :
10.1109/ICoSP.2012.6491591