Predictors for high frequency processes based on rational polynomials approximation of periodic exponentials

TL;DR

This paper introduces linear integral predictors for high-frequency signals with spectral gaps, utilizing rational polynomial approximations of complex exponentials to improve prediction accuracy in continuous time.

Contribution

It proposes a novel predictor design based on rational polynomial approximation of periodic exponentials, addressing high-frequency signal prediction with spectral gaps.

Findings

Effective predictor design demonstrated for signals with spectral gaps

Improved prediction accuracy using rational polynomial approximation

Applicable to continuous-time high-frequency signal processing

Abstract

The paper presents linear integral predictors for continuous time high-frequency signals with a a finite spectrum gap. The predictors are based on approximation of a complex valued periodic exponential (complex sinusoid) by rational polynomials.