An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform

Dianwei Wang, Jing Wang, Ying Liu, Zhijie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Recent studies show that Cohen class bilinear time-frequency distribution methods do not have satisfactory denoising performance when analyzing multi-component LFM signals. This paper has constructed a new adaptive time-frequency filtering factor and has proposed an adaptive time-frequency filtering algorithm based on generalized S-transform. Firstly, the time-frequency distribution is obtained by transforming the time domain signals to time-frequency domain by using generalized S-transform, which is followed by calculating instantaneous frequency based on the phase information from the time-frequency distribution. Secondly, the time-frequency distribution regions occupied by clustered energy of effective signal are identified through time-frequency region extraction method and all time-frequency distribution spectrum out of the regions are removed. Thirdly, a novel TF filtering factor is constructed by the time-frequency concentration characteristic to restrain the random noise components in the regions of effective signal. Finally, the filtered signals are retrieved by using inverse generalized S-transform. Simulation results demonstrate that the proposed filtering algorithm has satisfactory performances for signal denoising which most features of original signal can be remained.

LanguageEnglish
Title of host publication2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780992680107
DOIs
Publication statusPublished - 30 Oct 2015
Event21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth - University of Strathclyde, Glasgow, United Kingdom
Duration: 11 Sep 201512 Sep 2015
Conference number: 21
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7301994 (Link to Conference Proceedings)

Conference

Conference21st International Conference on Automation and Computing
Abbreviated titleICAC
CountryUnited Kingdom
CityGlasgow
Period11/09/1512/09/15
Internet address

Fingerprint

S-transform
Filtering
Mathematical transformations
Signal denoising
Denoising
Time Domain
Instantaneous Frequency
Random Noise
Generalized Inverse
Frequency Domain

Cite this

Wang, D., Wang, J., Liu, Y., & Xu, Z. (2015). An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform. In 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015 [7314000] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IConAC.2015.7314000
Wang, Dianwei ; Wang, Jing ; Liu, Ying ; Xu, Zhijie. / An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform. 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc., 2015.
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title = "An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform",
abstract = "Recent studies show that Cohen class bilinear time-frequency distribution methods do not have satisfactory denoising performance when analyzing multi-component LFM signals. This paper has constructed a new adaptive time-frequency filtering factor and has proposed an adaptive time-frequency filtering algorithm based on generalized S-transform. Firstly, the time-frequency distribution is obtained by transforming the time domain signals to time-frequency domain by using generalized S-transform, which is followed by calculating instantaneous frequency based on the phase information from the time-frequency distribution. Secondly, the time-frequency distribution regions occupied by clustered energy of effective signal are identified through time-frequency region extraction method and all time-frequency distribution spectrum out of the regions are removed. Thirdly, a novel TF filtering factor is constructed by the time-frequency concentration characteristic to restrain the random noise components in the regions of effective signal. Finally, the filtered signals are retrieved by using inverse generalized S-transform. Simulation results demonstrate that the proposed filtering algorithm has satisfactory performances for signal denoising which most features of original signal can be remained.",
keywords = "generalized S-transform, multi-component LFM signal, time-frequency concentration characteristic, time-frequency filtering",
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Wang, D, Wang, J, Liu, Y & Xu, Z 2015, An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform. in 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015., 7314000, Institute of Electrical and Electronics Engineers Inc., 21st International Conference on Automation and Computing, Glasgow, United Kingdom, 11/09/15. https://doi.org/10.1109/IConAC.2015.7314000

An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform. / Wang, Dianwei; Wang, Jing; Liu, Ying; Xu, Zhijie.

2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7314000.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform

AU - Wang, Dianwei

AU - Wang, Jing

AU - Liu, Ying

AU - Xu, Zhijie

PY - 2015/10/30

Y1 - 2015/10/30

N2 - Recent studies show that Cohen class bilinear time-frequency distribution methods do not have satisfactory denoising performance when analyzing multi-component LFM signals. This paper has constructed a new adaptive time-frequency filtering factor and has proposed an adaptive time-frequency filtering algorithm based on generalized S-transform. Firstly, the time-frequency distribution is obtained by transforming the time domain signals to time-frequency domain by using generalized S-transform, which is followed by calculating instantaneous frequency based on the phase information from the time-frequency distribution. Secondly, the time-frequency distribution regions occupied by clustered energy of effective signal are identified through time-frequency region extraction method and all time-frequency distribution spectrum out of the regions are removed. Thirdly, a novel TF filtering factor is constructed by the time-frequency concentration characteristic to restrain the random noise components in the regions of effective signal. Finally, the filtered signals are retrieved by using inverse generalized S-transform. Simulation results demonstrate that the proposed filtering algorithm has satisfactory performances for signal denoising which most features of original signal can be remained.

AB - Recent studies show that Cohen class bilinear time-frequency distribution methods do not have satisfactory denoising performance when analyzing multi-component LFM signals. This paper has constructed a new adaptive time-frequency filtering factor and has proposed an adaptive time-frequency filtering algorithm based on generalized S-transform. Firstly, the time-frequency distribution is obtained by transforming the time domain signals to time-frequency domain by using generalized S-transform, which is followed by calculating instantaneous frequency based on the phase information from the time-frequency distribution. Secondly, the time-frequency distribution regions occupied by clustered energy of effective signal are identified through time-frequency region extraction method and all time-frequency distribution spectrum out of the regions are removed. Thirdly, a novel TF filtering factor is constructed by the time-frequency concentration characteristic to restrain the random noise components in the regions of effective signal. Finally, the filtered signals are retrieved by using inverse generalized S-transform. Simulation results demonstrate that the proposed filtering algorithm has satisfactory performances for signal denoising which most features of original signal can be remained.

KW - generalized S-transform

KW - multi-component LFM signal

KW - time-frequency concentration characteristic

KW - time-frequency filtering

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DO - 10.1109/IConAC.2015.7314000

M3 - Conference contribution

BT - 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

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Wang D, Wang J, Liu Y, Xu Z. An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform. In 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7314000 https://doi.org/10.1109/IConAC.2015.7314000