A flexible algorithm for extracting periodic signals

Authors:
Zhi-Lin Zhang;Haitao Meng
Affiliations:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Electric and Information Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

In this paper, we propose a flexible two-stage algorithm for extracting desired periodic signals. In the first stage, if the period and phase information of the desired signal is available (or can be estimated), a minimum mean square error approach is used to coarsely recover the desired source signal. If only the period information is available (or can be estimated), a robust correlation based method is proposed to achieve the same goal. The second stage uses a higher-order statistics based Newton-like algorithm, derived from a constrained maximum likelihood criteria, to process the extracted noisy signal as cleanly as possible. A parameterized nonlinearity is adopted in this stage, adapted according to the estimated statistics of the desired signal. Compared with many existing extraction algorithms, the proposed algorithm has better performance, which is confirmed by simulations.