Data Classification Algorithms for Complex Heavy-Tailed Distributions

This paper deals with the adaptive classification problem of heavy-tailed data drawn from a multivariate distribution of the complex elliptically symmetric family. The authors assume that the collected data is either homogeneous or partially homogeneous. Based on the GLRT criterion, two adaptive classifiers are devised for the scenarios of Hermitian and persymmetric scatter matrices, respectively. To estimate the unknown parameters, iterative algorithms with convergence property are introduced to solve the developed non-convex maximum likelihood optimization problems. The simulation results show that, compared with the Hermitian scenario, the exploitation of persymmetric structure information can lead to an obvious performance improvement.