Coastal Radar Target Recognition Based On Kinematic Data (AIS) With Machine Learning
Raphael Ginoulhac, Frédéric Barbaresco, Jean-Yves Schneider, Jean-Marie Pannier, Sebastien Savary · 2019
We propose a simple yet efficient method to classify targets using kinematic data only. We use data obtained from the Automatic Identification System (AIS) to get labelled trajectories for supervised learning, as a proof of concept for later use on radar tracks. The method consists in extracting statistical features from each temporal variable (speed, acceleration, etc.), and then feeding them to a Gradient Boosting classifier. We show that the performance of this method is on par with the state of the art, as the classification accuracy is close to 86% with 7 classes, and thus that it could be used in radars for the classification of targets based on their trajectory.