Paper Title:
Exponential Sampling: A Gibbs Phenomena Removal Model for Finite Rate of Innovation Sampling Framework
  Abstract

We propose an exponential approximating function as a sampling kernel with finite rate of innovation. The performance of reconstruct non-bandlimited signals from its low frequency components would inevitably induce Gibbs phenomenon. This paper establishes the theoretical model on relationship between sampling kernel filter and parametric reconstruction method of non-bandlimited signals, and designs a new window function exponential sampling kernel filter to removal Gibbs Influence. Simulation results show that, compared to Sinc sampling kernel filter, the reconstruction ability of exponential filter based finite rate sampling system is improved under the white Gaussian noise environment.

  Info
Periodical
Edited by
Han Zhao
Pages
470-474
DOI
10.4028/www.scientific.net/AMM.130-134.470
Citation
H. Qian, L. Yu, "Exponential Sampling: A Gibbs Phenomena Removal Model for Finite Rate of Innovation Sampling Framework", Applied Mechanics and Materials, Vols. 130-134, pp. 470-474, 2012
Online since
October 2011
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