Sampling of stochastic operators

TL;DR

This paper introduces a sampling method to identify stochastic operators with limited autocorrelation support, using responses to known signals to reconstruct the operator or its autocorrelation.

Contribution

It presents a novel sampling approach for stochastic operators constrained by autocorrelation support, enabling operator reconstruction from deterministic test signals.

Findings

Effective sampling methodology for stochastic operators.

Reconstruction of operators from response data.

Applicable to operators with autocorrelation support restrictions.

Abstract

We develop sampling methodology aimed at determining stochastic operators that satisfy a support size restriction on the autocorrelation of the operators stochastic spreading function. The data that we use to reconstruct the operator (or, in some cases only the autocorrelation of the spreading function) is based on the response of the unknown operator to a known, deterministic test signal.