Model-based detector and extraction of weak signal frequencies from chaotic data

作者：Zhou Cangtao^*; Cai Tianxing; Lai Choy Heng; Wang Xingang; Lai Ying Cheng

来源：Chaos, 2008, 18(1): 013104.

DOI：10.1063/1.2827500

Detecting a weak signal from chaotic time series is of general interest in science and engineering. In this work we introduce and investigate a signal detection algorithm for which chaos theory, nonlinear dynamical reconstruction techniques, neural networks, and time-frequency analysis are put together in a synergistic manner. By applying the scheme to numerical simulation and different experimental measurement data sets (Henon map, chaotic circuit, and NH(3) laser data sets), we demonstrate that weak signals hidden beneath the noise floor can be detected by using a model-based detector. Particularly, the signal frequencies can be extracted accurately in the time-frequency space. By comparing the model-based method with the standard denoising wavelet technique as well as supervised principal components analysis detector, we further show that the nonlinear dynamics and neural network-based approach performs better in extracting frequencies of weak signals hidden in chaotic time series.

• 出版日期2008-3
• 单位北京工商大学; 北京应用物理与计算数学研究所; 北京大学