Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology

TL;DR

This paper introduces a novel metaplectic Cohen's distribution framework for enhanced time-frequency analysis and denoising of non-stationary signals, utilizing adaptive filtering in the Wigner distribution domain.

Contribution

It develops a unified convolution type of metaplectic Cohen's distribution and an adaptive filtering method that automatically adjusts kernel functions for improved noise suppression.

Findings

Outperforms traditional Wiener filter in noise reduction

Effectively suppresses high levels of additive noise

Demonstrates superior denoising in non-stationary signals

Abstract

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we blend time-frequency operators and coordinate operator fractionizations to formulate the definition of the metaplectic Wigner distribution, based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the metaplectic Wigner distribution domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including Wiener filter and fixed kernel functions-based or adaptive Cohen's distribution in noise suppression.