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CNN-32DC: An improved radar-based drone recognition system based on Convolutional Neural Network by Ann Janeth Garcia; Ali Aouto; Jae-Min Lee; Dong-Seong Kim is a Engineering article available to read on EtoBox.

What is CNN-32DC: An improved radar-based drone recognition system based on Convolutional Neural Network about?

This paper proposes a system that will guard infrastructures against incoming threats from drones by detecting it with the use of a radar device-based detection scheme. The database acquired, named Real Doppler RAD-DAR (Radar with Digital Array Receiver) is constructed by a Microwave and Radar Group. The radar used uses a Frequency Modulated Continuous Wave (FMCW) on an 8.75 GHz based frequency band with a BW m a x of 500 MHz. The proposed Convolutional Neural Network (CNN), CNN-32DC is varied with different number of filters, combination layers, and number of feature extraction blocks, the preference that will give the most accurate result was selected and compared with different machine learning and classification learning algorithms gained an accuracy that exceeds other networks with less processing time.

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Author

Ann Janeth Garcia; Ali Aouto; Jae-Min Lee; Dong-Seong Kim

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)