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Proceedings Paper

Signal extrapolation in noisy data with wavelet representation
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Paper Abstract

A new approach for signal extrapolation based on wavelet representation known as scale-time limited extrapolation and a denoising process is investigated in this research. We first examine a new signal modeling technique using wavelets and the corresponding scale-time limited signal extrapolation algorithm. Then, the sensitivity of the algorithm to noise is discussed, and a denoising algorithm based on the time-localization property of the wavelet transform is proposed. By integrating the denoising process and the iterative scale-time limited extrapolation algorithm, we obtain a very robust signal extrapolation algorithm for noisy data. A simulation result of signal extrapolation from noisy observed data is presented to illustrate the performance of the proposed robust signal extrapolation algorithm.

Paper Details

Date Published: 1 November 1993
PDF: 12 pages
Proc. SPIE 2027, Advanced Signal Processing Algorithms, Architectures, and Implementations IV, (1 November 1993); doi: 10.1117/12.160431
Show Author Affiliations
Li-Chien Lin, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 2027:
Advanced Signal Processing Algorithms, Architectures, and Implementations IV
Franklin T. Luk, Editor(s)

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