Papers Published
Abstract:
This paper proposes a recursive diffeomorphism-based regression method for the one-dimensional generalized mode decomposition problem that aims at extracting generalized modes αk(t)sk(2πNkφk(t)) from their superpositionKk=1 αk(t)sk(2πNkφk(t)). We assume that the instantaneous information, e.g., αk(t) and Nkφk(t), is determined by, e.g., a one-dimensional synchrosqueezed transform or some other methods. Our main contribution is to propose a novel approach based on diffeomorphisms and nonparametric regression to estimate wave shape functions sk(t). This leads to a framework for the generalized mode decomposition problem under a weak well-separation condition. Numerical examples of synthetic and real data are provided to demonstrate the successful application of our approach.