Sparse time-frequency representation of nonlinear and nonstationary data

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 {ɑ(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

Additional Information

© 2013 Science China Press and Springer-Verlag Berlin Heidelberg. Received May 16, 2013; accepted September 10, 2013. This work was supported by Air Force Office of Scientific Research, Multidisciplinary University Research Initiative, USA (Grant No. FA9550-09-1-0613), Department of Energy of USA (Grant No. DE-FG02-06ER25727), Natural Science Foundation of USA (Grant No. DMS-0908546) and National Natural Science Foundation of China (Grant No. 11201257). The authors would like to thank Professors Norden E. Huang and Zhaohua Wu for many stimulating discussions on EMD/EEMD and topics related to the research presented here. The authors would also like to thank Professors Ingrid Daubechies, Stanley Osher, and Zuowei Shen for their interest in this work and for a number of valuable discussions.

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