Hardware Deployable Radar Spectrum-based CNN classifier for Drone targets

KTH Rheinmetall Italia Sp.A, Italy (creator_code:org_t)

Institute of Electrical and Electronics Engineers Inc. 2023
2023
English.
In: 20th European Radar Conference, EuRAD 2023. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 131-134

Related links:: https://urn.kb.se/re...; show more...; https://doi.org/10.2...; show less...

Abstract Subject headings

It is well known that AI technology is spreading its application in all technical fields. Among all, radar field may take benefit from AI usage because its ability to recognize the environment by using small amount of domain expertise. Nowadays, the upcoming drone threats are stimulating the radar engineers to seek for advanced solutions for their classification in a congested scenario. As Deep Learning based drone classification has shown its first promising results, still few studies aim at hardware implementation of neural networks trained on frequency-amplitude 1D spectra. In this study, a lightweight CNN is trained on radar collected data prior to be quantized and evaluated on FPGA hardware.

NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)

By the author/editor: Gaizo, Dario Del; De Palo, Frances ...; Cipriani, Fabio; Giancane, Luca

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Enginee ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Embedded Systems

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