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Sparse time-frequency representation of nonlinear and nonstationary data

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Abstract

Adaptive data analysis provides an important tool in extracting hidden physical information from multiscale data that arise from various applications. In this paper, we review two data-driven time-frequency analysis methods that we introduced recently to study trend and instantaneous frequency of nonlinear and nonstationary data. These methods are inspired by the empirical mode decomposition method (EMD) and the recently developed compressed (compressive) sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form {a(t) cos(θ(t))}, where a is assumed to be less oscillatory than cos(θ(t)) and θ′ ⩾ 0. This problem can be formulated as a nonlinear l 0 optimization problem. We have proposed two methods to solve this nonlinear optimization problem. The first one is based on nonlinear basis pursuit and the second one is based on nonlinear matching pursuit. Convergence analysis has been carried out for the nonlinear matching pursuit method. Some numerical experiments are given to demonstrate the effectiveness of the proposed methods.

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Authors and Affiliations

  1. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA

    Thomas Yizhao Hou

  2. Mathematical Sciences Center, Tsinghua University, Beijing, 100084, China

    ZuoQiang Shi

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  1. Thomas Yizhao Hou
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  2. ZuoQiang Shi
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Correspondence to Thomas Yizhao Hou.

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Dedicated to Professor Shi Zhong-Ci on the Occasion of his 80th Birthday

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Hou, T.Y., Shi, Z. Sparse time-frequency representation of nonlinear and nonstationary data. Sci. China Math. 56, 2489–2506 (2013). https://doi.org/10.1007/s11425-013-4733-7

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