Synchrosqueezed Wave Packet Transforms and Diffeomorphism Based Spectral Analysis for 1D General Mode Decompositions

TL;DR

This paper introduces advanced synchrosqueezed wave packet transforms and diffeomorphism-based spectral analysis techniques for improved 1D mode decomposition, offering higher resolution and applicability to complex signals.

Contribution

It presents novel algorithms for 1D mode decomposition using synchrosqueezed wave packet transforms and diffeomorphism-based spectral analysis, enhancing resolution and handling general shape functions.

Findings

Better resolution than wavelet-based methods in time-frequency analysis

Accurate estimation of instantaneous information of well-separated modes

Effective decomposition of synthetic and real data signals

Abstract

This paper develops new theory and algorithms for 1D general mode decompositions. First, we introduce the 1D synchrosqueezed wave packet transform and prove that it is able to estimate the instantaneous information of well-separated modes from their superposition accurately. The synchrosqueezed wave packet transform has a better resolution than the synchrosqueezed wavelet transform in the time-frequency domain for separating high frequency modes. Second, we present a new approach based on diffeomorphisms for the spectral analysis of general shape functions. These two methods lead to a framework for general mode decompositions under a weak well-separation condition and a well different condition. Numerical examples of synthetic and real data are provided to demonstrate the fruitful applications of these methods.