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Learning Strategies for Radar Clutter Classification
Pia Addabbo, Sudan Han, Danilo Orlando, Giuseppe Ricci · 2020-04-17 · via eess.SP updates on arXiv.org

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.

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