scispace - formally typeset
Open AccessProceedings ArticleDOI

Device identification using active noise interrogation and RF-DNA "fingerprinting" for non-destructive amplifier acceptance testing

Reads0
Chats0
TLDR
In this paper, 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.
Abstract
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.

read more

Citations
More filters
Proceedings ArticleDOI

Imaging time series for internet of things radio frequency fingerprinting

TL;DR: A novel approach to the RF fingerprinting of Internet of Things (IOT) devices, where the time series are converted into images, out of which image processing features are extracted, provides a better identification accuracy than conventional sets of statistical features used in the literature.
Proceedings ArticleDOI

The application of the Symbolic Aggregate Approximation algorithm (SAX) to radio frequency fingerprinting of IoT devices

TL;DR: A novel approach to RF fingerprinting based on the application to time series of the Symbolic Aggregate Approximation algorithm (SAX) is investigated, demonstrating that a SAX-based approach provides a very high identification accuracy (over 99%), and turns out to be attractive, as compared to classification without SAX, from both a computational standpoint and its robustness to noise.
Journal ArticleDOI

Antenna Classification Using Gaussian Mixture Models (GMM) and Machine Learning

TL;DR: This paper adopts Gaussian Mixture Models (GMM) technique as feature extraction approach and firstly applies it to extract RFF of antennas to demonstrate better performance on large datasets with classification accuracy above 88% using a SVM classifier.

Near Real-Time Zigbee Device Discrimination Using CB-DNA Features

TL;DR: This research found that PHY characteristics provide an additional method of authentication NRT to enhance the inherent network security for Zigbee applications from cyberattacks.

A Non-Destructive Evaluation Application Using Software Defined Radios and Bandwidth Expansion

TL;DR: This research investigated the reconstruction of simultaneous SDR receiver instantaneous bandwidth (sub-band) collections using single, dual and multiple SDR receivers and found a bandwidth expansion technique that exploits a priori transmit signal knowledge and auto-correlation provides a solution.
References
More filters
Proceedings ArticleDOI

Attacks on physical-layer identification

TL;DR: The feasibility of performing impersonation attacks on the modulation-based and transient-based fingerprinting techniques are studied to improve access control in wireless networks, revent device cloning and complement message authentication protocols.
Journal ArticleDOI

Application of wavelet-based RF fingerprinting to enhance wireless network security

TL;DR: WD fingerprinting with DT-CWT features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy, relative to time domain (TD) RF fingerprinting.
Journal ArticleDOI

Intrinsic Physical-Layer Authentication of Integrated Circuits

TL;DR: Radio-frequency distinct native attribute (RF-DNA) fingerprinting is adapted as a physical-layer technique to improve the security of integrated circuit (IC)-based multifactor authentication systems and is promising for use in a wide variety of related security problems.
Proceedings ArticleDOI

Physical layer identification of embedded devices using RF-DNA fingerprinting

TL;DR: RF distinct native attribute (RF-DNA) fingerprinting is introduced as a means to uniquely identify embedded processors and other integrated circuit devices by passively monitoring and exploiting unintentional RF emissions.

Exploitation of RF-DNA for device classification and verification using GRLVQI processing

TL;DR: This dissertation introduces a GRLVQI classifier into an RF-DNA fingerprinting process and demonstrates applicability for device classification and ID verification and Dimensional Reduction Analysis (DRA) to enhance the experimental-to-operational transition potential of RF- DNA fingerprinting.
Related Papers (5)
Trending Questions (1)
How to Repair Ahuja amplifier?

This method can be used during amplifier production to quickly identify and isolate faulty device production.