General sampling theorems for functions in reproducing kernel Hilbert spaces
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In this paper we prove general sampling theorems for functions belonging to a reproducing kernel Hilbert space (RKHS) which is also a closed subspace of a particular Sobolev space. We present details of this approach as applied to the standard sampling theory and its extension to nonuniform sampling. The general theory for orthogonal sampling sequences and nonorthogonal sampling sequences is developed. Our approach includes as concrete cases many recent extensions, for example, those based on the Sturm-Liouville transforms, Jacobi transforms, Laguerre transforms, Hankel transforms, prolate spherical transforms, etc., finite-order sampling theorems, as well as new sampling theorems obtained by specific choices of the RKHS. In particular, our setting includes nonorthogonal sampling sequences based on the theory of frames. The setting and approach enable us to consider various types of errors (truncation, aliasing, jitter, and amplitude error) in the same general context.
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- General sampling theorems for functions in reproducing kernel Hilbert spaces
Mathematics of Control, Signals and Systems
Volume 4, Issue 4 , pp 363-390
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- Sampling theorems
- Reproducing kernels
- Bandlimited signals
- Nonuniform sampling
- Nonorthogonal sampling sequences
- Sampling errors