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A Classification Algorithm of Moving Military Vehicle

Classification of moving military vehicle in battlefield is an important part of information acquirement. Support vector machine is a pattern classification method which is suitable to solve the small sample, non-linear classification problems. This paper uses one-versus-one multi-class SVM to classify military vehicle. This method is based on multi-sensor data including noise signal, the magnetic field disturbance signal, and vibration signal. The parameters of the SVM are determined by using the cross-validation method. The Simulation experiment results show that, compared to AdaBoost algorithm and two-class SVM, the one-versus-one multi-class SVM has higher accuracy.