We introduce a new adaptive method for analyzing nonlinear and nonstationary data.
This method is inspired by the empirical mode decomposition (EMD) method and the
recently developed compressed 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 ≥ 0 is assumed to be
smoother than cos(θ(t)) and θ is a piecewise smooth increasing function. We formulate
this problem as a nonlinear L1 optimization problem. Further, we propose an iterative
algorithm to solve this nonlinear optimization problem recursively. We also introduce
an adaptive filter method to decompose data with noise. Numerical examples are given
to demonstrate the robustness of our method and comparison is made with the EMD
method. One advantage of performing such a decomposition is to preserve some intrinsic
physical property of the signal, such as trend and instantaneous frequency. Our method
shares many important properties of the original EMD method. Because our method is
based on a solid mathematical formulation, its performance does not depend on numerical
parameters such as the number of shifting or stop criterion, which seem to have a
major effect on the original EMD method. Our method is also less sensitive to noise
perturbation and the end effect compared with the original EMD method.