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Advanced classification techniques for drone payloads

Francesco Fioranelli, Julien Le Kernec

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

Abstract

This chapter presents a summary of radar-based classification approaches developed for small drones carrying payloads. Specific focus is given to three types oftechniques that were validated on the same multistatic radar data set collected usingthe University College London (UCL)-netted radar NetRAD. These techniquesused, respectively, features extracted from the centre of mass and bandwidth of themicro-Doppler signatures; different radar data domains generated from the micro-Doppler data to be processed by pretrained Convolutional Neural Networks(CNNs) and spectral kurtosis analysis on the micro-Doppler.
Original languageEnglish
Title of host publicationRadar Countermeasures for Unmanned Aerial Vehicles
PublisherInstitution of Engineering and Technology
Chapter11
Pages339-362
Number of pages24
ISBN (Electronic)9781839531903
ISBN (Print)9781839531903
Publication statusPublished - 2021

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