Vehicles classification using Z-score and modelling neural network for forward scattering radar

Author

Abdullah, Nor Fadzilah ; Rashid, N.E.A. ; Othman, K.A. ; Musirin, I.

Author_Institution

Fac. of Electr. Eng., Univ. Teknol. Mara, Shah Alam, Malaysia

fYear

2014

fDate

16-18 June 2014

Firstpage

1

Lastpage

4

Abstract

This paper presents the improvement of vehicle classification in forward scattering radar (FSR) using a new classification technique. The technique is a combination between two methods which are Z-score and neural network (NN). The Z-score is used to extract the features of target signature while neural network is used as a classifier to classify the size of vehicles. The results of vehicle classification for three different frequencies in this case 64, 151 and 434 MHz are presented and discussed. The results reveal that the proposed technique is effective to perform ground target classification.

Keywords

electromagnetic wave scattering; feature extraction; neural nets; radar signal processing; signal classification; FSR; Z-score; forward scattering radar; frequency 151 MHz; frequency 434 MHz; frequency 64 MHz; ground target classification; neural network modelling; target signature feature extraction; vehicle size classification technique; Artificial neural networks; Feature extraction; Radar; Testing; Training; Vehicles; Z-score; forward scattering radar (FSR); neural network (NN);

fLanguage

English

Publisher

ieee

Conference_Titel

Radar Symposium (IRS), 2014 15th International

Conference_Location

Gdansk

Print_ISBN

978-617-607-552-3

Type

conf

DOI

10.1109/IRS.2014.6869280

Filename

6869280