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.

• Periodical: Applied Mechanics and Materials (Volumes 130-134)
• Pages: 470-474
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.130-134.470
• Online since: October 2011
• Authors: Hui Qian, Lun Yu
• Keywords: Dirac Streams, Finite Innovation Rate, Gibbs Phenomenon, Sampling Kernel Filter, Sampling Theory

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