Matrix Extension for Pathological Radar Clutter Machine Learning
Language
en
Document de travail - Pré-publication
English Abstract
This paper deals with radar clutter statistical learning based on spatial Doppler fluctuation. In articles [1]-[4], data is clustered cell by cell. In this article, we generalize the previous model to extract information ...Read more >
This paper deals with radar clutter statistical learning based on spatial Doppler fluctuation. In articles [1]-[4], data is clustered cell by cell. In this article, we generalize the previous model to extract information not only from each cell independently, but also from the cells spatial correlation. We first introduce the radar data, then the model and efficient tools to estimate the model parameters. The model parameters will be shown to be Hermitian Positive Definite Block-Toeplitz matrices. Next we endow the manifold of Hermitian Positive Definite Block-Toeplitz matrices with a Riemannian metric coming from information geometry. Finally, we adapt a supervised classification algorithm (the k-Nearest Neighbors) and an unsupervised classification algorithm (the Agglomerative Hierarchical Clustering) to this Riemannian manifold.Read less <
English Keywords
Index Terms-Radar clutter
multidimensional signals
spatio- temporal correlation
machine learning
Information geometry
Riemannian manifold
Block-Toeplitz matrices
Siegel disk
Origin
Hal imported