Radar emitters classification and clustering with a scale mixture of normal distributions

Abstract

In this study, a scale mixture of normal distributions model is developed for classification and clustering of radar emitters. A radar signal is characterised by a pulse-to-pulse modulation pattern and is often partially observed. The proposed model can classify and cluster different radar emitters even in the presence of outliers and missing values. The classification method, based on a mixture model, focuses on the introduction of latent variables that give us the possibility to handle sensitivity of the model to outliers and to allow a less restrictive modelling of missing data. A Bayesian treatment is adopted for model learning, supervised classification and clustering. The inference is processed through a variation Bayesian approximation. Some numerical experiments on realistic data show that the proposed method provides more accurate results than state-of-the-art classification algorithms.