Classification performance using 'RF-DNA' fingerprinting of ultra-wideband noise waveforms

Authors

• Mathew W. Lukacs, Air Force Institute of Technology
• Peter J. Collins, Air Force Institute of Technology
• Michael A. Temple, Air Force Institute of Technology

Document Type

Article

Publication Date

5-1-2015

Abstract

Excerpt: Device classification is important in many applications such as industrial quality control, through-wall imaging and network security. A novel approach has been proposed to use a digital noise radar (DNR) to actively interrogate microwave devices and classify defective units using `radio frequency distinct native attribute (RF-DNA)' fingerprinting and various classifier algorithms. Abstract ©IET.

Comments

© 2020 The Institution of Engineering and Technology

The "Link to Full Text" on this page opens or downloads the full article (PDF), as hosted at the journal's website on Wiley.

Co-author Mathew Lukacs was an AFIT PhD student at the time of this article. (DTIC R&E: AD1018264, September 2016 graduate)

DOI

10.1049/el.2015.0051

Source Publication

Electronics Letters (ISSN 0013-5194)

Recommended Citation

Lukacs, M., Collins, P. and Temple, M. (2015), Classification performance using 'RF-DNA' fingerprinting of ultra-wideband noise waveforms. Electron. Lett., 51: 787-789. https://doi.org/10.1049/el.2015.0051