High Resolution Time-Frequency Generation with Generative Adversarial Networks

TL;DR

This paper introduces a novel high-resolution time-frequency representation method using a Conditional Generative Adversarial Network (CGAN) to improve signal feature identification in applications like radar imaging.

Contribution

The paper presents a new reassignment technique employing CGANs to enhance time-frequency representations beyond existing methods.

Findings

CGAN-based reassignment produces higher resolution TF representations.

The method outperforms traditional reassignment techniques.

Enhanced signal feature clarity in radar applications.

Abstract

Signal representation in Time-Frequency (TF) domain is valuable in many applications including radar imaging and inverse synthetic aparture radar. TF representation allows us to identify signal components or features in a mixed time and frequency plane. There are several well-known tools, such as Wigner-Ville Distribution (WVD), Short-Time Fourier Transform (STFT) and various other variants for such a purpose. The main requirement for a TF representation tool is to give a high-resolution view of the signal such that the signal components or features are identifiable. A commonly used method is the reassignment process which reduces the cross-terms by artificially moving smoothed WVD values from their actual location to the center of the gravity for that region. In this article, we propose a novel reassignment method using the Conditional Generative Adversarial Network (CGAN). We train a CGAN to perform the reassignment process. Through examples, it is shown that the method generates high-resolution TF representations which are better than the current reassignment methods.