  In this paper, a generalized multivariate Student-t mixture model is
developed for classification and clustering of Low Probability of Intercept
radar waveforms. A Low Probability of Intercept radar signal is characterized
by a pulse compression waveform which is either frequency-modulated or
phase-modulated. The proposed model can classify and cluster different
modulation types such as linear frequency modulation, non linear frequency
modulation, polyphase Barker, polyphase P1, P2, P3, P4, Frank and Zadoff codes.
The classification method focuses on the introduction of a new prior
distribution for the model hyper-parameters that gives us the possibility to
handle sensitivity of mixture models to initialization and to allow a less
restrictive modeling of data. Inference is processed through a Variational
Bayes method and a Bayesian treatment is adopted for model learning, supervised
classification and clustering. Moreover, the novel prior distribution is not a
well-known probability distribution and both deterministic and stochastic
methods are employed to estimate its expectations. Some numerical experiments
show that the proposed method is less sensitive to initialization and provides
more accurate results than the previous state of the art mixture models.
