Toeplitz Hermitian Positive Definite Matrix Machine Learning based on Fisher Metric
Langue
en
Chapitre d'ouvrage
Ce document a été publié dans
Geometric Science of Information, Geometric Science of Information. 2019-08-27p. 261-270
Résumé en anglais
Here we propose a method to classify radar clutter from radar data using an unsupervised classification algorithm. The data will be represented by Positive Definite Hermitian Toeplitz matrices and clustered using the Fisher ...Lire la suite >
Here we propose a method to classify radar clutter from radar data using an unsupervised classification algorithm. The data will be represented by Positive Definite Hermitian Toeplitz matrices and clustered using the Fisher metric. Once the clustering algorithm dispose of a large radar database, new radars will be able to use the experience of other radars, which will improve their performances: learning radar clutter can be used to fix some false alarm rate created by strong echoes coming from hail, rain, waves, mountains, cities; it will also improve the detectability of slow moving targets, like drones, which can be hidden in the clutter, flying close to the landform.< Réduire
Mots clés en anglais
Burg algorithm
au- tocorrelation matrix
k-means
unsupervised classification
machine learning
radar clutter
Kähler metric
reflection coefficients
Origine
Importé de halUnités de recherche