@inproceedings{e7ddcec2dbff4f9d99213a11c5322a50,
title = "Learning Based Noise Identification Techniques Using Time-Frequency Analysis and the U-Net",
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.",
keywords = "U-net, deep learning, noise removal, radar signal, time-frequency analysis",
author = "Wang, {Chih Hao} and Ding, {Jian Jiun} and Chang, {Chieh Sheng} and Ouyang, {Liang Yu}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 ; Conference date: 03-12-2019 Through 06-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ISPACS48206.2019.8986251",
language = "???core.languages.en_GB???",
series = "Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019",
}