The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Spatiospectral Concentration on a Sphere
Open accessAbstract
We pose and solve the analogue of Slepian's time-frequency concentration problem on the surface of the unit sphere to determine an orthogonal family of strictly bandlimited functions that are optimally concentrated within a closed region of the sphere or, alternatively, of strictly spacelimited functions that are optimally concentrated in the spherical harmonic domain. Such a basis of simultaneously spatially and spectrally concentrated functions should be a useful data analysis and representation tool in a variety of geophysical and planetary applications, as well as in medical imaging, computer science, cosmology, and numerical analysis. The spherical Slepian functions can be found by solving either an algebraic eigenvalue problem in the spectral domain or a Fredholm integral equation in the spatial domain. The associated eigenvalues are a measure of the spatiospectral concentration. When the concentration region is an axisymmetric polar cap, the spatiospectral projection operator commutes with a Sturm--Liouville operator; this enables the eigenfunctions to be computed extremely accurately and efficiently, even when their area-bandwidth product, or Shannon number, is large. In the asymptotic limit of a small spatial region and a large spherical harmonic bandwidth, the spherical concentration problem reduces to its planar equivalent, which exhibits self-similarity when the Shannon number is kept invariant.
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Cites
Spectrum estimation and harmonic analysis
Thomson, D. J. (1982) · Proceedings of the IEEE · doi:10.1109/proc.1982.12433 · Cited by 4,418
Prolate Spheroidal Wave Functions, Fourier Analysis and Uncertainty - I
Slepian, D., & Pollak, H. O. (1961) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1961.tb03976.x · Cited by 2,492
Spectral Analysis for Physical Applications
Percival, D. B., & Walden, A. T. (1993) · Cambridge University Press eBooks · doi:10.1017/cbo9780511622762 · Cited by 2,161
Prolate Spheroidal Wave Functions, Fourier Analysis, and Uncertainty-V: The Discrete Case
Slepian, D. (1978) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1978.tb02104.x · Cited by 1,741
Uncertainty Principles and Signal Recovery
Donoho, D. L., & Stark, P. B. (1989) · SIAM Journal on Applied Mathematics · doi:10.1137/0149053 · Cited by 1,054
Necessary density conditions for sampling and interpolation of certain entire functions
Landau, H. J. (1967) · Acta Mathematica · doi:10.1007/bf02395039 · Cited by 843
Prolate Spheroidal Wave Functions, Fourier Analysis and Uncertainty - II
Landau, H. J., & Pollak, H. O. (1961) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1961.tb03977.x · Cited by 839
Landau, H. J., & Pollak, H. O. (1962) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1962.tb03279.x · Cited by 839 · cited 1×
“In a classic series of papers published in the 1960s, Slepian, Landau, and Pollak solved a fundamental problem in information theory, namely, that of optimally concentrating a given signal in both the time and frequency domains [35, 36, 58, 59].”
Slepian, D. (1964) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1964.tb01037.x · Cited by 763
Time-frequency localization operators: a geometric phase space approach
Daubechies, I. (1988) · IEEE Transactions on Information Theory · doi:10.1109/18.9761 · Cited by 649
Metadata
- Authors
- Simons, F. J., Dahlen, F. A., & Wieczorek, M. A.
- Venue
- SIAM Review
- Published
- 2006-01
- Volume
- 48
- Issue
- 3
- Pages
- 504-536
- ISSN
- 0036-1445 (ISSN-L)
- 1095-7200
- OpenAlex
- W2046193047
- Cited by
- 314
- References
- 35
- FWCI
- 15.93
- Type
- journal article
- Language
- en
- Open access
- green
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