Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4051
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dc.contributor.authorMandal, Priti-
dc.contributor.authorRoy, Lakshi Prosad-
dc.contributor.authorDas, Santos Kumar-
dc.date.accessioned2023-08-01T10:28:12Z-
dc.date.available2023-08-01T10:28:12Z-
dc.date.issued2023-07-
dc.identifier.citationWorld Conference on Communication & Computing (WCONF), Raipur, India, 14-16 July 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4051-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractUnmanned aerial vehicles (UAVs)/Drones have been broadly used in modern civilization over the past few years due to their low cost and ease of accessibility, which has raised concerns about privacy and security. It needs to classify flying objects, such as helicopters, birds, UAV/drone, etc., in order to maintain a watchful eye on the invader UAV/drone in the restricted area. In this paper classification of the flying object is done using Hybrid Convolutional Neural Network-Memetic (CNN-Memetic) Algorithm based on MicroDoppler Signature (MDS) for various arrangement of radar array in order to verify the significance of direction of signal received. The evaluation is done based on data acquired from the radar borne on the drone by varying different specifications.en_US
dc.subjectClassificationen_US
dc.subjectConvolutional Neural NetworkMemetic (CNN-Memetic) Algorithmen_US
dc.subjectMicro-Doppler Signature (MDS)en_US
dc.subjectRadaren_US
dc.titleClassification of Flying Objects Using Data from UAV Mounted Radaren_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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