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An Adaptive Time-frequency Filtering Algorithm for Multi-component LFM Signals based on Generalized S-transform

Wang, Dianwei, Wang, Jing, Liu, Ying and Xu, Zhijie (2015) An Adaptive Time-frequency Filtering Algorithm for Multi-component LFM Signals based on Generalized S-transform. In: 21st IEEE International Conference on Automation and Computing, 11-12th September 2015, University of Strathclyde, Glasgow, UK. (Unpublished)

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Recent studies show that Cohen class bilinear time-frequency distribution methods do not have satisfactory denoising performance when analyzing multicomponent LFM signals. This paper has constructed a new adaptive time-frequency iltering 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 usinggeneralized Stransform, which is followed by calculating instantaneous frequency based on the phase information from the timefrequency 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 Stransform. Simulation results demonstrate that the proposed filtering algorithm has satisfactory performances for signal denoising which most features of original signal can be remained.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Schools: School of Computing and Engineering > High-Performance Intelligent Computing > Visualisation, Interaction and Vision
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Depositing User: Jing Wang
Date Deposited: 20 Jul 2015 14:13
Last Modified: 25 Mar 2016 03:56


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