Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4051
Title: Classification of Flying Objects Using Data from UAV Mounted Radar
Authors: Mandal, Priti
Roy, Lakshi Prosad
Das, Santos Kumar
Keywords: Classification
Convolutional Neural NetworkMemetic (CNN-Memetic) Algorithm
Micro-Doppler Signature (MDS)
Radar
Issue Date: Jul-2023
Citation: World Conference on Communication & Computing (WCONF), Raipur, India, 14-16 July 2023
Abstract: Unmanned 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.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4051
Appears in Collections:Conference Papers

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