Chebychev moments based Drone Classification, Recognition and Fingerprinting
Carmine Clemente (University of Strathclyde)
Luca Pallotta (University of Roma Tre)
Christos Ilioudis (University of Strathclyde)
Francesco Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
Gaetano Giunta (University of Roma Tre)
Alfonso Farina (Consultant, Selex ES (retired) Rome)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This paper introduces the use of a Chebychev moments' based feature for micro-Doppler based Classification, Recognition and Fingerprinting of Drones. This specific feature has been selected for its low computational cost and orthogonality property. The capability of the proposed feature extraction framework is assessed at three different levels of major classification steps, namely classification, recognition and fingerprinting, demonstrating the effectiveness of the proposed approach to discriminate drones from birds, fixed wings from multi-rotors and drones carrying different payloads on real measured radar data.