A generalized multivariate Student-t mixture model for Bayesian classification and clustering of radar waveforms

TL;DR

This paper introduces a generalized multivariate Student-t mixture model for classifying and clustering radar waveforms, improving robustness and accuracy over existing models through a novel prior and Bayesian inference.

Contribution

It develops a new prior distribution for hyper-parameters and applies Variational Bayes for robust classification and clustering of radar signals.

Findings

Less sensitive to initialization

More accurate classification results

Effective handling of diverse modulation types

Abstract

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.