Poisson summation formula | EPFL Graph Search

In mathematics, the Poisson summation formula is an equation that relates the Fourier series coefficients of the periodic summation of a function to values of the function's continuous Fourier transform. Consequently, the periodic summation of a function is completely defined by discrete samples of the original function's Fourier transform. And conversely, the periodic summation of a function's Fourier transform is completely defined by discrete samples of the original function. The Poisson summation formula was discovered by Siméon Denis Poisson and is sometimes called Poisson resummation. Forms of the equation Consider an aperiodic function s(x) with Fourier transform S(f) \triangleq \int_{-\infty}^{\infty} s(x)\ e^{-i2\pi fx}, dx, alternatively designated by \hat s(f) and \mathcal{F}{s}(f). The basic Poisson summation formula is: Also consider periodic functions, where parameters T>0

Shift-Invariant and Sampling Spaces Associated With the Fractional Fourier Transform Domain

Ayush Bhandari

Shift-invariant spaces play an important role in sampling theory, multiresolution analysis, and many other areas of signal and image processing. A special class of the shift-invariant spaces is the class of sampling spaces in which functions are determined by their values on a discrete set of points. One of the vital tools used in the study of sampling spaces is the Zak transform. The Zak transform is also related to the Poisson summation formula and a common thread between all these notions is the Fourier transform.

2012

Generalized Poisson Summation Formula for Tempered Distributions

Quy Ha Nguyen, Michaël Unser

The Poisson summation formula (PSF), which relates the sampling of an analog signal with the periodization of its Fourier transform, plays a key role in the classical sampling theory. In its current forms, the formula is only applicable to a limited class of signals in

L _{ 1 }

. However, this assumption on the signals is too strict for many applications in signal processing that require sampling of non-decaying signals. In this paper we generalize the PSF for functions living in weighted Sobolev spaces that do not impose any decay on the functions. The only requirement is that the signal to be sampled and its weak derivatives up to order 1∕2 + ε for arbitrarily small ε > 0, grow slower than a polynomial in the

L _{ 2 }

sense. The generalized PSF will be interpreted in the language of distributions.

IEEE2015

Generalized Poisson Summation Formulas for Continuous Functions of Polynomial Growth

Quy Ha Nguyen, Michaël Unser, John Paul Ward

The Poisson summation formula (PSF) describes the equivalence between the sampling of an analog signal and the periodization of its frequency spectrum. In engineering textbooks, the PSF is usually stated formally without explicit conditions on the signal for the formula to hold. By contrast, in the mathematics literature, the PSF is commonly stated and proven in the pointwise sense for various types of

L _{ 1 }

signals. This

L _{ 1 }

assumption is, however, too restrictive for many signal-processing tasks that demand the sampling of possibly growing signals. In this paper, we present two generalized versions of the PSF for d-dimensional signals of polynomial growth. In the first generalization, we show that the PSF holds in the space of tempered distributions for every continuous and polynomially growing signal. In the second generalization, the PSF holds in a particular negative-order Sobolev space if we further require that d∕2 + ε derivatives of the signal are bounded by some polynomial in the

L _{ 2 }

sense.

2017