Construction of signal-dependent Cohen's-class time-frequency distributions using iterative blind deconvolution

Andrew E Yagle; Jose E. Torres-Fernandez

doi:10.1117/12.504467

24 December 2003 Construction of signal-dependent Cohen's-class time-frequency distributions using iterative blind deconvolution

Andrew E Yagle, Jose E. Torres-Fernandez

Author Affiliations +

Proceedings Volume 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII; (2003) https://doi.org/10.1117/12.504467
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

The problem of kernel design for Cohen time-frequency distributions is formulated as a blind deconvolution problem. It is shown that the iterative blind deconvolution method (IBDM) used in image restoration problems can be successfully applied to solve the kernel design problem. We obtain the following results: (1) the rate of convergence depends on which domains the constraints are imposed (2) certain constraints are needed for algorithm convergence (3) the more constrained the kernel design is, the faster the rate of convergence (4) there are tradeoffs between constraints, e.g., compact support vs. satisfaction of marginals; (5) time-frequency distributions which are more amenable to visual interpretation can be obtained using this algorithm.

Citation Download Citation

Andrew E Yagle and Jose E. Torres-Fernandez "Construction of signal-dependent Cohen's-class time-frequency distributions using iterative blind deconvolution", Proc. SPIE 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII, (24 December 2003); https://doi.org/10.1117/12.504467

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