Moving Target Classification with Radar Point-Clouds and Supervised Contrastive Learning

Abstract: This thesis deals with radar data for the purpose of moving target classification in the context of surveillance. The radar data in question comes in the form of point-clouds represented as frame-wise histograms with several channels and we seek to improve upon an existing cross-entropy based deep learning classifier using supervised contrastive loss. We find that the embeddings output by supervised contrastive loss exhibit clearer separation between the classes and that it is possible to combine this approach with principal component analysis to obtain greater classification scores.