Automatic target classifier for a Ground Surveillance Radar using linear discriminant analysis and Logistic regression

Author

Javed, Azhar ; Ejaz, Aqib ; Liaqat, Sidrah ; Ashraf, A. ; Ihsan, M.B.

Author_Institution

Coll. of Electr. & Mech. Eng., Nat. Univ. of Sci. & Technol., Islamabad, Pakistan

fYear

2012

fDate

Oct. 31 2012-Nov. 2 2012

Firstpage

302

Lastpage

305

Abstract

This paper presents the design of an automatic target classifier for a Ground Surveillance Radar namely NUST Radar* (NR-V3). The classifier is developed to distinguish between pedestrians, vehicles and no target (noise) classes. Feature vectors are extracted from the FFT spectrum of radar audio signal. Logistic regression and linear discriminant analysis based classifiers are used for classification of feature vectors. The classifiers are trained and tested using radar data collected with NR-V3. Overall classification accuracy of 95.6% and 92% is achieved for Logistic regression and linear discriminant analysis classifiers respectively.

Keywords

feature extraction; radar signal processing; radar target recognition; regression analysis; search radar; FFT spectrum; NUST radar; automatic target classifier; classification accuracy; feature vector classification; feature vector extraction; ground surveillance radar; linear discriminant analysis; logistic regression; radar audio signal; Classification algorithms; Doppler radar; Logistics; Support vector machine classification; Surveillance; Vehicles; Automatic target classification; Ground surveillance radar; Linear discriminant analysis; Logistic regression; Principal component analysis; Pulsedoppler radar; Radar audio signal;

fLanguage

English

Publisher

ieee

Conference_Titel

Radar Conference (EuRAD), 2012 9th European

Conference_Location

Amsterdam

Print_ISBN

978-1-4673-2471-7

Type

conf

Filename

6450732