In this paper, we address the problem of classifying clutter returns in order to partition them into statistically homogeneous subsets. The classification procedure relies on a model for the observables including latent variables that is solved by the expectation-maximization algorithm. The derivations are carried out by accounting for three different cases for the structure of the clutter covariance matrix. A preliminary performance analysis highlights that the proposed technique is a viable means to cluster clutter returns over the range.
@article{arxiv.2004.08277,
title = {Learning Strategies for Radar Clutter Classification},
author = {Pia Addabbo and Sudan Han and Danilo Orlando and Giuseppe Ricci},
journal= {arXiv preprint arXiv:2004.08277},
year = {2020}
}