Modulation Classification of MPSK Signals Based on Relevance Vector Machines

In this paper, a new classification method based on relevance vector machine (RVM) is used in the MPSK signals classification. Compared with the support vector machine (SVM), RVM is sparse model in the Bayesian framework, not only the solution is highly sparse, but also it does not need to adjust model parameter and its kernel functions don´t need to satisfy Mercer´s condition. The fourth order cumulants of the received signals are used as the classification vector firstly, and then multi-class classifier of RVM is designed. We first introduce the sparse Bayesian classification model, then transform the RVM learning to the maximization of marginal likelihood, and select the fast sequential sparse Bayesian learning algorithm. Through the experiment compared with SVM classifier proves the advantage of RVM.