A Sparse Representation of Random Signals

TL;DR

This paper extends adaptive Fourier decomposition (AFD) and pre-orthogonal AFD (POAFD) to random signals, providing sparse representations useful for practical analysis of stochastic signals in one and multiple dimensions.

Contribution

It introduces a novel generalization of AFD and POAFD methods to handle random signals, broadening their applicability beyond deterministic signals.

Findings

Developed AFD-type sparse representation for one-dimensional random signals.

Extended sparse representation techniques to multivariate random signals in stochastic Hilbert spaces.

Demonstrated the effectiveness of the proposed methods in practical signal analysis.

Abstract

Studies of sparse representation of deterministic signals have been well developed. Amongst there exists one called adaptive Fourier decomposition (AFD) established through adaptive selections of the parameters defining a Takenaka-Malmquist system in one-complex variable. The AFD type algorithms give rise to sparse representations of signals of finite energy. The multivariate generalization of AFD is one called pre-orthogonal AFD (POAFD), the latter being established with the context Hilbert space possessing a dictionary. The purpose of the present study is to generalize both AFD and POAFD to random signals. We work on two types of random signals. One is those expressible as the sum of a deterministic signal with an error term such as a white noise; and the other is, in general, as mixture of several classes of random signals obeying certain distributive law. In the first part of the paper we develop an AFD type sparse representation for one-dimensional random signals by making use analysis of one complex variable. In the second part, without complex analysis, we treat multivariate random signals in the context of stochastic Hilbert space with a dictionary. Like in the deterministic signal case the established random sparse representations are powerful tools in practical signal analysis.