Radar discrimination of small airborne targets through kinematic features and machine learning

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dc.contributor.authorDoumard, Timothée
dc.contributor.authorGañán Riesco, Fabio
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorPanagiotakopoulos, Dimitrios
dc.contributor.authorBennett, Cameron
dc.contributor.authorHarman, Stephen
dc.date.accessioned2022-11-10T13:39:00Z
dc.date.available2022-11-10T13:39:00Z
dc.date.issued2022-10-31
dc.description.abstractThis work studies binary classification problem for small airborne targets (drones vs other) by means of their trajectory analysis. For this purpose a set of the kinematic features extracted from drone trajectories using radar detections with a classification scheme that utilises Random Forests is proposed. The development is based on experimental data acquired from the Holographic radar from Aveillant Ltd. An approach for real-time classification is proposed, where an adaptive sliding window procedure is employed to make predictions over time from trajectories. Several models utilising different kinematic features (angle, slope, velocity, and their combination) are studied. The best model achieves an accuracy of more than 95%. In addition, fundamental issues with imbalanced datasets in the context of this topic are raised and illustrated using the collected data.en_UK
dc.identifier.citationDoumard T, Gañán Riesco F, Petrunin I, et al., (2022) Radar discrimination of small airborne targets through kinematic features and machine learning. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), 18-22 September 2022, Portsmouth, Virginia, USAen_UK
dc.identifier.eisbn978-1-6654-8607-1
dc.identifier.eissn2155-7209
dc.identifier.isbn978-1-6654-8608-8
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC55683.2022.9925778
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18696
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDroneen_UK
dc.subjectclassificationen_UK
dc.subjecttrajectoryen_UK
dc.subjectmotionen_UK
dc.subjectmachine learningen_UK
dc.titleRadar discrimination of small airborne targets through kinematic features and machine learningen_UK
dc.typeConference paperen_UK

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