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Training Data Classification Algorithms for Radar Applications
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DOI：10.1109/LSP.2019.2935625.png)
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Abstract
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In this letter, the problem of environment classification in the radar context is addressed. Specifically, adaptive architectures are conceived to classify training data, used for covariance estimation, as either homogeneous or heterogeneous. Such architectures are based upon the generalized likelihood ratio test criterion and exploit three covariance matrix structures (i.e., Hermitian, persymmetric, and symmetric structures). Numerical examples based on both synthetic and real data confirm the effectiveness of the proposed algorithms. It is important to highlight that the proposed architectures might represent a preliminary stage whose decisions can be used to select a suitable covariance estimate for target detection purposes.
Keywords:
Generalized likelihood ratio test
persymmetric structure
radar environment classification
spectral symmetry
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