Non-supervised Machine Learning Algorithms for Radar Clutter High-Resolution Doppler Segmentation and Pathological Clutter Analysis

Here we propose a method to classify radar clutter from radar data using a non-supervised classification algorithm. Thus new radars will be able to use the experience of other radars, which will improve their performance: learning pathological radar clutter can be used to fix some false alarm rate created by strong echoes coming from hail, rain, waves, montains, 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.