Chebychev moments based Drone Classification, Recognition and Fingerprinting

Conference paper (2021)

Authors

Carmine Clemente University of Strathclyde
Luca Pallotta University of Roma Tre
Christos Ilioudis University of Strathclyde
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Gaetano Giunta University of Roma Tre
Alfonso Farina Consultant, Selex ES (retired) Rome
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2021 Carmine Clemente, Luca Pallotta, Christos Ilioudis, F. Fioranelli, Gaetano Giunta, Alfonso Farina
DOI
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https://doi.org/10.23919/IRS51887.2021.9466211
Classification Micro-Doppler Fingerprinting UAVs Recognition ATR Drones Image moments DAVs
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

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.

Files

09466211.pdf
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