Classification of sums of complex exponentials

TL;DR

This paper introduces a robust classification method for signals modeled as sums of complex exponentials, combining analytical models with data-driven learning to improve accuracy under uncertainties, demonstrated with radar data.

Contribution

It proposes a novel classification strategy that integrates analytical modeling with machine learning, enhancing robustness against uncertainties and perturbations.

Findings

Effective classification of complex exponential signals.

Robust performance under modeling uncertainties.

Validated with radar scattering data.

Abstract

Numerous signals in relevant signal processing applications can be modeled as a sum of complex exponentials. Each exponential term entails a particular property of the modeled physical system, and it is possible to define families of signals that are associated with the complex exponentials. In this paper, we formulate a classification problem for this guiding principle and we propose a data processing strategy. In particular, we exploit the information obtained from the analytical model by combining it with data-driven learning techniques. As a result, we obtain a classification strategy that is robust under modeling uncertainties and experimental perturbations. To assess the performance of the new scheme, we test it with experimental data obtained from the scattering response of targets illuminated with an impulse radio ultra-wideband radar.