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

Cangtao Zhou, Tianxing Cai, Choy Heng Lai, Xingang Wang, Ying-Cheng Lai

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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 (H́non map, chaotic circuit, and NH3 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.

Original languageEnglish (US)
Article number013104
Issue number1
StatePublished - 2008

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • General Physics and Astronomy
  • Applied Mathematics


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