TWR signals de-noising by using WNN

Author

Xiaoli, Chen ; Mao, Tian ; Jing, Guo

Author_Institution

Sch. of Electron. Inf., Wuhan Univ., Wuhan, China

Volume

1

fYear

2010

fDate

6-7 March 2010

Firstpage

280

Lastpage

283

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 de-noising algorithm can achieve good de-noising performance and hold the useful detail of TWR signals.

Keywords

fault tolerance; neural nets; radar signal processing; signal denoising; wavelet transforms; TWR signals denoising; WNN; artificial neural networks; fault-tolerance; self- learning; through-the-wall radar signals; time-frequency localization; wavelet neural networks; wavelet transform; Feedforward neural networks; Neural networks; Noise reduction; RF signals; Radar; Receiving antennas; Reflector antennas; Signal denoising; Transmitting antennas; Wavelet transforms; de-noising; feed forward neural network; through-wall radar (TWR); wavelet neural networks (WNN); wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on

Conference_Location

Wuhan

ISSN

1948-3414

Print_ISBN

978-1-4244-5192-0

Electronic_ISBN

1948-3414

Type

conf

DOI

10.1109/CAR.2010.5456845

Filename

5456845