Abstract
We present a panorama describing the pervasiveness of the short-time Fourier transform (STFT) in a host of topics including the following: waveform design and optimal ambiguity function behavior for radar and communications applications; vector-valued ambiguity function theory for multi-sensor environments; finite Gabor frames for deterministic compressive sensing and as a background for the HRT conjecture; generalizations of Fourier frames and non-uniform sampling; and pseudo-differential operator frame inequalities.
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Acknowledgements
The first named author gratefully acknowledges the support of MURI-ARO Grant W911NF- 09-1-0383, NGA Grant 1582-08-1-0009, and DTRA Grant HDTRA1-13-1-0015. The second named author gratefully acknowledges the support of the Norbert Wiener Center at the University of Maryland, College Park.
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Appendix
Appendix
The Classical Sampling Theorem goes back to papers by Cauchy (1840s), see [13, Theorem 3.10.10] for proofs of Theorem 17. It has had a significant impact on various topics in mathematics, including number theory and interpolation theory, long before Shannon’s application of it in communications.
Theorem 17 (Classical Sampling Theorem).
Let T,Ω > 0 satisfy the condition that 0 < 2TΩ ≤ 1, and let s be an element of the Paley-Wiener space \(PW_{1/(2T)}\) satisfying the condition that \(\hat{s} = S = 1\) on [−Ω,Ω] and \(S \in L^{\infty }(\hat{\mathbb{R}})\). Then
where the convergence of (5.18) is in the \(L^{2}(\mathbb{R})\) norm and uniformly in \(\mathbb{R}\). One possible sampling function s is
We can compute Fourier transforms numerically using the following result, whose proof, see [14], requires Theorem 17.
Theorem 18.
Let T,Ω > 0 satisfy the property that 2TΩ = 1, let N ≥ 2 be an even integer, and let \(f \in PW_{\varOmega } \cap L^{1}(\mathbb{R})\) . Consider the dilation f T (t) = Tf(Tt) as a continuous function on \(\mathbb{R}\) , as well as a function on \(\mathbb{Z}\) defined by m ↦ f T [m], where f T [m] = f T (m). Assume that \(f_{T} \in \ell^{1}(\mathbb{Z})\) . Then for every integer \(n \in [-\frac{N} {2}, \frac{N} {2} ]\) , we have
where W N = e −2πi∕N and \((f_{T})_{N}^{\circ }[m] =\sum _{k\in \mathbb{Z}}f_{T}[m - kN]\) .
In practice, the computation (5.19) requires natural error estimates and the FFT.
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Benedetto, J.J., Begué, M.J. (2015). Fourier Operators in Applied Harmonic Analysis. In: Pfander, G. (eds) Sampling Theory, a Renaissance. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-19749-4_5
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