Slepian functions and their use in signal estimation and spectral analysis

TL;DR

This paper reviews Slepian functions, which are mathematically designed to be localized in both time (or space) and frequency, facilitating spectral analysis of scientific data constrained to specific regions.

Contribution

It provides a comprehensive overview of Slepian functions, their theoretical basis, and their application in signal estimation and spectral analysis across various domains.

Findings

Slepian functions enable effective localized spectral analysis.

They lead to practical algorithms for one, two dimensions, and spherical surfaces.

The framework improves analysis of spatially or temporally bounded data.

Abstract

It is a well-known fact that mathematical functions that are timelimited (or spacelimited) cannot be simultaneously bandlimited (in frequency). Yet the finite precision of measurement and computation unavoidably bandlimits our observation and modeling scientific data, and we often only have access to, or are only interested in, a study area that is temporally or spatially bounded. In the geosciences we may be interested in spectrally modeling a time series defined only on a certain interval, or we may want to characterize a specific geographical area observed using an effectively bandlimited measurement device. It is clear that analyzing and representing scientific data of this kind will be facilitated if a basis of functions can be found that are "spatiospectrally" concentrated, i.e. "localized" in both domains at the same time. Here, we give a theoretical overview of one particular approach to this "concentration" problem, as originally proposed for time series by Slepian and coworkers, in the 1960s. We show how this framework leads to practical algorithms and statistically performant methods for the analysis of signals and their power spectra in one and two dimensions, and on the surface of a sphere.