Learning Based Noise Identification Techniques Using Time-Frequency Analysis and the U-Net

Chih Hao Wang, Jian Jiun Ding, Chieh Sheng Chang, Liang Yu Ouyang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In wireless communication, it is inevitable that the signal is highly affected by the noise. For example, for the radar located in the sea shore, due to the effects of sea clutter and remote detection range, the signal to noise ratio (SNR) is only about 015 dB. In this manuscript, we develop an advanced noise determination and removal algorithm based on the deep learning method of the U-net. The U-net is a pixel-wise classification network and widely used in image segmentation. In this work, we find that it is also an effective way to determine whether a pixel in the time-frequency domain is the signal part or the noise part, even in the low SNR case. It is very helpful for reducing the noise effect and improving the accuracy of fundamental frequency analysis for radar signal processing.

Original languageEnglish
Title of host publicationProceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728130385
DOIs
StatePublished - Dec 2019
Event2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 - Taipei, Taiwan
Duration: 3 Dec 20196 Dec 2019

Publication series

NameProceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

Conference

Conference2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
Country/TerritoryTaiwan
CityTaipei
Period3/12/196/12/19

Keywords

  • deep learning
  • noise removal
  • radar signal
  • time-frequency analysis
  • U-net

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