Gabor-Atom networks based radar target identification

Author

Shi, Yn ; Zhang, Man-Da

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

Volume

3

fYear

2000

fDate

2000

Firstpage

1980

Abstract

A Gabor-Atom network (GAN) approach for application in radar target recognition is proposed. The Gabor atoms selected by a multilayer feedforward neural network extract discriminant features among different classes of radar target returns. The self-learning mechanism is used not only for the network but for the feature parameters. Results on the classification of microwave anechoic chamber data of three different scaled airplane models are presented

Keywords

feature extraction; feedforward neural nets; radar computing; radar target recognition; signal classification; time-frequency analysis; Gabor transform; Gabor-Atom networks; discriminant features extraction; microwave anechoic chamber data; multilayer feedforward neural network; radar range profiles; radar target identification; radar target recognition; radar target returns; scaled airplane models; self-learning mechanism; time-frequency analysis; Airplanes; Anechoic chambers; Data mining; Feature extraction; Feedforward neural networks; Gallium nitride; Multi-layer neural network; Neural networks; Radar applications; Target recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-5747-7

Type

conf

DOI

10.1109/ICOSP.2000.893494

Filename

893494