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Proceedings Paper

Maximum matching initial selection for adaptive Gaussian chirplet decomposition
Author(s): Guizhou Lyu; Qiang He
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Paper Abstract

Adaptive Gaussian Chirplet Decomposition (AGCD) is a time-frequency signal decomposition algorithm with high resolution. The Gaussian chirplet basis adopted has variable time width, frequency center with linear chirp, which has both good time and frequency energy localization. But this basis is not orthogonal, and the computation in searching basises when decomposing a signal is very huge. AGCD can reduce computation by convert the optimization process to a traditional curve-fitting problem. But the performance of the AGCD is highly dependent on the initial selection. Traditional energy based initial selection fails in some cases when two or more basis has deep cross. The proposed maximum matching based initial selection is a fast and accurate basis searching algorithm, which choose the best correlated basis each time within several candidates. Simulation results show that the new algorithm is much more stable and accurate than the energy based one without increasing computation.

Paper Details

Date Published: 6 July 2015
PDF: 5 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963129 (6 July 2015); doi: 10.1117/12.2197095
Show Author Affiliations
Guizhou Lyu, Mechanical Engineering College (China)
Qiang He, Mechanical Engineering College (China)


Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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