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arXiv · 2004.08277

Learning Strategies for Radar Clutter Classification

Pia Addabbo·Sudan Han·Danilo Orlando·Giuseppe Ricci
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Abstract

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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Pia Addabbo, Sudan Han, Danilo Orlando, Giuseppe Ricci. 2020-04-17. Learning Strategies for Radar Clutter Classification. https://arxiv.org/abs/2004.08277

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