TWR signals de-noising by using WNN

Authors:
Chen Xiaoli;Tian Mao;Guo Jing
Affiliations:
School of Electronic Information, Wuhan University, Wuhan, China;School of Electronic Information, Wuhan University, Wuhan, China;School of Electronic Information, Wuhan University, Wuhan, China
Venue:
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Year:
2010

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Abstract

The de-noising issue of through-the-wall radar (TWR) signal is an essential TWR's performance on detecting lives. This paper introduces TWR signal de-noising algorithm based on a wavelet neural networks (WNN). WNN owns the property of time-frequency localization of wavelet transform, as well as the excellent characteristics of artificial neural networks, self-learning and fault-tolerance, which make it a powerful tool for removing noises from noisy through-the-wall radar signals. Experimental results show that the proposed WNN based denoising algorithm can achieve good de-noising performance and hold the useful detail of TWR signals.