Classification of radar targets using synthetic neural networks

Author

Jouny, I. ; Garber, E.D. ; Ahalt, S.C.

Author_Institution

Dept. of Electr. Eng., Lafayette Coll., Easton, PA, USA

Volume

29

Issue

2

fYear

1993

fDate

4/1/1993 12:00:00 AM

Firstpage

336

Lastpage

344

Abstract

Radar target classification performance of neural networks is evaluated. Time-domain and frequency-domain target features are considered. The sensitivity of the neural network algorithm to changes in network topology and training noise level is examined. The problem of classifying radar targets at unknown aspect angles is considered. The performance of the neural network algorithms is compared with that of decision-theoretic classifiers. Neural networks can be effectively used as radar target classification algorithms with an expected performance within 10 dB (worst case) of the optimum classifier

Keywords

backpropagation; feature extraction; neural nets; radar theory; FSCL; algorithms; backpropagation; classification performance; frequency-domain target features; maximum likelihood algorithm; network topology; optimum classifier; radar targets; synthetic neural networks; time domain target features; training noise; unknown aspect angles; winner takes all layer; Backscatter; Fourier transforms; Frequency measurement; Nearest neighbor searches; Network topology; Neural networks; Noise level; Pattern recognition; Radar measurements; Time domain analysis;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/7.210072

Filename

210072