Toeplitz Hermitian Positive Definite Matrix Machine Learning based on Fisher Metric
Language
en
Chapitre d'ouvrage
This item was published in
Geometric Science of Information, Geometric Science of Information. 2019-08-27p. 261-270
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Burg algorithm
au- tocorrelation matrix
k-means
unsupervised classification
machine learning
radar clutter
Kähler metric
reflection coefficients
Origin
Hal imported