Classification performance using 'RF‐DNA' fingerprinting of ultra‐wideband noise waveforms
Mathew Lukacs, Peter J. Collins, Michael A. Temple · Electronics Letters · 2015
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.