IF equation: a feature extractor for high-concentration time-frequency representation of mixed signals

TL;DR

This paper introduces a unified framework using the IF equation for high-concentration time-frequency analysis, improving TF sharpness and unifying various existing methods for analyzing complex non-stationary signals.

Contribution

It systematically explains the theoretical basis of IF equation-based TF analysis and proposes a novel method to combine IF equations for enhanced signal representation.

Findings

The IF equation unifies IF and group delay estimators.

Many TF post-processing methods are categorized under IF equation-based techniques.

Numerical and practical experiments demonstrate improved TF sharpness and energy concentration.

Abstract

High-concentration time-frequency (TF) representation provides a valuable tool for characterizing multi-component non-stationary signals. In our previous work, we proposed using an instantaneous frequency (IF) equation to sharpen the TF distribution, and experiments verified its effectiveness. In this paper, we systematically discuss why the IF equation-based TF analysis methods work and how to use the IF equation to improve TF sharpness. By the analysis of the properties of the IF equation, we prove that a good IF equation can unify the well-known IF and group delay estimators and provides an effective way to characterize the mixture of time-varying and frequency-varying signals. By discussing the post-processing techniques based on the IF equation, we can prove that many popular TF post-processing methods, such as the synchroextracting transform, the multi-synchrosqueezing transform, and the time extracting transform, fall into the IF equation-based category. We also propose a novel approach to combine different IF equations to minimize energy spreading based on local sparsity. Numerical simulations and practical experiments are presented to illustrate the performance of the proposed IF equation-based TF analysis method.