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

Abstract

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. RF-DNA has previously demonstrated 'serial number' discrimination of numerous passive radio frequency signals, achieving classification accuracies above 80% using multiple discriminant analysis/maximum likelihood (MDA/ML) and generalised relevance learning vector quantisation-improved (GRLVQI) classifiers. It has also demonstrated above 80% classification of limited active interrogation responses with a DNR signal using these classifiers. The performance capabilities of the two different classifiers, MDA/ML and GRLVQI, on RF-DNA fingerprints produced from the ultra-wideband noise radar correlation response is expanded.