Recursive kernels for time-frequency signal representations.

Time-frequency distribution kernels which satisfy the desirable time-frequency properties and simultaneously allow recursive implementations of the local autocorrelation and the ambiguity functions are computationally efficient and prove valuable for on-line processing. We introduce a class of recursive kernels which apply modified comb filters at different timelags. The generalized Hamming, Blackman, and Half-Sine kernels are members of this class. These kernels have well known lowpass filter characteristics, lead to computational invariance under the kernel extent, and compete in performance with existing nonrecursive t-f kernels.

Main Author: Amin, Moeness G.
Format: Villanova Faculty Authorship
Language: English
Published: 1996
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173588
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dc_source_str_mv IEEE SIGNAL PROCESSING LETTERS, VOL. 3, NO. 1, JANUARY 1996.
author Amin, Moeness G.
author_facet_str_mv Amin, Moeness G.
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author_s Amin, Moeness G.
spellingShingle Amin, Moeness G.
Recursive kernels for time-frequency signal representations.
author-letter Amin, Moeness G.
author_sort_str Amin, Moeness G.
dc_title_str Recursive kernels for time-frequency signal representations.
title Recursive kernels for time-frequency signal representations.
title_short Recursive kernels for time-frequency signal representations.
title_full Recursive kernels for time-frequency signal representations.
title_fullStr Recursive kernels for time-frequency signal representations.
title_full_unstemmed Recursive kernels for time-frequency signal representations.
collection_title_sort_str recursive kernels for time-frequency signal representations.
title_sort recursive kernels for time-frequency signal representations.
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description Time-frequency distribution kernels which satisfy the desirable time-frequency properties and simultaneously allow recursive implementations of the local autocorrelation and the ambiguity functions are computationally efficient and prove valuable for on-line processing. We introduce a class of recursive kernels which apply modified comb filters at different timelags. The generalized Hamming, Blackman, and Half-Sine kernels are members of this class. These kernels have well known lowpass filter characteristics, lead to computational invariance under the kernel extent, and compete in performance with existing nonrecursive t-f kernels.
publishDate 1996
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dc_date_str 1996
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fgs.label Recursive kernels for time-frequency signal representations.
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dc.title Recursive kernels for time-frequency signal representations.
dc.creator Amin, Moeness G.
dc.description Time-frequency distribution kernels which satisfy the desirable time-frequency properties and simultaneously allow recursive implementations of the local autocorrelation and the ambiguity functions are computationally efficient and prove valuable for on-line processing. We introduce a class of recursive kernels which apply modified comb filters at different timelags. The generalized Hamming, Blackman, and Half-Sine kernels are members of this class. These kernels have well known lowpass filter characteristics, lead to computational invariance under the kernel extent, and compete in performance with existing nonrecursive t-f kernels.
dc.date 1996
dc.format Villanova Faculty Authorship
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dc.source IEEE SIGNAL PROCESSING LETTERS, VOL. 3, NO. 1, JANUARY 1996.
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