A Classification Algorithm of Moving Military Vehicle

Abstract:

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.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2043-2046
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2043
• Online since: July 2014
• Authors: Yun Fei Ma*, Feng Chen
• Keywords: Feature Extraction, Simulation, Statistical Learning, SVM, Vehicle Classification

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