Performance of radar target recognition schemes using neural networks-a comparative study

Author

Nandagopal, D. ; Martin, N.M. ; Johnson, R.P. ; Lozo, P. ; Palaniswami, M.

Author_Institution

Guided Weapons Div., Defence Sci. & Technol. Organ., Salisbury, SA, Australia

Volume

ii

fYear

1994

fDate

19-22 Apr 1994

Abstract

Doppler signatures of experimental radar targets have been obtained and processed using conventional signal processing techniques to extract characteristic features. Radar target recognition using adaptive resonance theory, learning vector quantiser, feedforward, and probabilistic neural networks has been attempted. The performance characteristics of the above neural architectures in classifying the experimental radar targets are discussed and the results of a comparative study presented

Keywords

ART neural nets; Doppler radar; feature extraction; feedforward neural nets; multilayer perceptrons; pattern classification; radar computing; radar signal processing; radar target recognition; self-organising feature maps; vector quantisation; Doppler signatures; adaptive resonance theory; comparative study; feedforward neural networks; learning vector quantiser; neural network; probabilistic neural networks; radar target recognition schemes; Artificial neural networks; Doppler radar; Neural networks; Neurons; Radar scattering; Radar signal processing; Radar tracking; Resonance; Target recognition; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389574

Filename

389574