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

Abstract:

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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.

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Periodical:

Edited by:

Han Zhao

Pages:

470-474

DOI:

10.4028/www.scientific.net/AMM.130-134.470

Citation:

H. Qian and 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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$35.00

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