Abstract:

A new approach to common signals classification of relevance vector machine (RVM) was presented and two signal classifiers based on kernel methods of support vector machine (SVM) and RVM were compared and analyzed. First several robust features of signals were extracted as the input of classifiers, then the kernel thought was used to map feature vectors impliedly to the high dimensional feature space, and multi-class RVM and SVM classifiers were designed to complete AM, CW, SSB, MFSK and MPSK signals recognition. Simulation result showed that when chose proper parameter, RVM and SVM had comparable accuracy but RVM had less learning time and basis functions. The classification speed of RVM is much faster than SVM.

Key words:

kernel function, sparse Bayesian model, RVM, SVM