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.
The cost of quality is critical to all industrial processes including microwave device production, which is often labor intensive and subject to production defects. Early defect detection can improve quality and reduce cost. A novel approach to defect detection has been demonstrated using a random noise radar (RNR), coupled with Radio Frequency Distinctive Native Attributes (RF-DNA) fingerprinting processing algorithms to non-destructively interrogate microwave devices. The RNR is uniquely suitable since it uses an Ultra Wideband (UWB) noise waveform as an active interrogation method that will not cause damage to sensitive microwave components and multiple RNRs can operate simultaneously in close proximity, allowing for significant parallelization of defect detection systems. Previous experimentation has demonstrated the ability to discern antenna loads and fault conditions, and identify faulty elements in a phased array antenna. This paper extends this method into identifying faulty conditions of devices behind a receive antenna such as typical microwave amplifiers. This method can be used during amplifier production to quickly identify and isolate faulty device production.
Quality control is critical for all industrial processes, but often times defect detection is labor intensive. A novel approach to industrial defect detection is proposed using a Digital Noise Radar (DNR), coupled with Radio Frequency Distinct Native Attribute (RF-DNA) fingerprinting processing algorithms to non-destructively interrogate microwave devices. The DNR is uniquely suitable since it uses an Ultra Wideband noise waveform as an active interrogation method that will not cause damage to sensitive microwave components. Additionally, it has been demonstrated that multiple DNRs can operate simultaneously in close proximity, allowing for significant parallelization of defect detection systems resulting in increased process throughput. Using this method, 100% sampling for quality control may be attainable in many cases. The ability to classify defective units from properly functioning units was demonstrated in [1] with potential applications including assembly line inspection of automotive collision avoidance systems, wireless or cellular antenna defect detection during manufacture, and phased array element defect detection prior to RF system assembly. However, prior research into active interrogation has been strictly empirical. This paper will develop an analytical model and simulation for interrogating a passively terminated antenna with an overall objective of improving classification performance through optimization of the interrogation signal bandwidth.
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