Radar target classification using support vector machine and subspace methods

Target classification is a significant research direction in radar field. The range profile is a good target electromagnetic scattering characteristic for real-time target classification. This study proposes a novel method which combines support vector machine (SVM) and subspace methods to achieve complex target classification. The performances of SVM and three representative subspace methods are analysed using range profiles generated by graphical electromagnetic computing method. Experimental results demonstrate that SVM classifier has better robustness in sample variation than conventional classifiers. The auxiliary effects of three subspace methods on classification have respective preponderances in different aspects.