System Approximations and Generalized Measurements in Modern Sampling Theory

Abstract

This paper studies several aspects of signal reconstruction of sampled data in spaces of bandlimited functions. In the first part, signal spaces are characterized in which the classical sampling series uniformly converge, and we investigate whether adaptive recovery algorithms can yield uniform convergence in spaces where nonadaptive sampling series does not. In particular, it is shown that the investigation of adaptive signal recovery algorithms needs completely new analytic tools since the methods used for non-adaptive reconstruction procedures, which are based on the celebrated Banach-Steinhaus theorem, are not applicable in the adaptive case. The second part analyzes the approximation of the output of stable linear timeinvariant (LTI) systems based on samples of the input signal, and where the input is assumed to belong to the Paley-Wiener space of bandlimited functions with absolute integrable Fourier transform. If the samples are acquired by point evaluations of the input signal f , then there exist stable LTI systems H such that the approximation process does not converge to the desired output H f even if the oversampling factor is arbitrarily large. If one allows generalized measurements of the input signal, then the output of every stable LTI system can be uniformly approximated in terms of generalized measurements of the input signal. The last section studies the situation where only the amplitudes of the signal samples are known. It is shown that one can find specific measurement functionals such that signal recovery of bandlimited signals from amplitude measurement is possible, with an overall sampling rate of four times the Nyquist rate.

The divergence behavior of adaptive signal processing algorithms with finite search horizon

“…for all f ∈ X . If a convergent subsequence {Nk}k∈N ⊂ N exists, it generally depends on the actual f ∈ X [6]. So the selection of a good subsequence {Nk = Nk(f )}k∈N such that…”