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

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.