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Proceedings Paper

Time-frequency-guided quadratic filters
Author(s): William J. Williams
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Paper Abstract

Time-frequency distributions (TFDs) of Cohen's class often dramatically reveal complex structures that are not evident in the raw signal. Standard linear filters are often not able to separate the underlying signal from background clutter and noise. The essense of the signal can often be extracted from the TFD by evaluating strategic slices through the TFD for a series of frequencies. However, TFDs are often computationally intense compared to other methods. This paper demonstrates that quadratic filters may be designed to capture the same information as is available in the specific slices through the TFD at a considerably lower computational cost. The outputs of these filters can be combined to provide a robust impulse-like response to the chosen signal. This is particularly useful when the exact time series representation of the signal is unknown, due to variations and background clutter and noise. It is also noted that Teager's method is closely related to TFDs and are an example of a quadratic filter. Results using an ideal matched filter and the TFD motivated quadratic filter are compared to give insight into their relative responses.

Paper Details

Date Published: 6 December 2002
PDF: 8 pages
Proc. SPIE 4791, Advanced Signal Processing Algorithms, Architectures, and Implementations XII, (6 December 2002); doi: 10.1117/12.455785
Show Author Affiliations
William J. Williams, Quantum Signal LLC (United States)
Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 4791:
Advanced Signal Processing Algorithms, Architectures, and Implementations XII
Franklin T. Luk, Editor(s)

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