A Linear Model for Classification of Electronic Devices Using Harmonic Radar

A new approach to exploit nonlinear reradiation of electronic circuits under test (ECUT) for classification of electronic devices using harmonic radar is proposed in this article. Unlike prior approaches, we develop a linear model to relate the measurements to the unknown parameters representing the...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2021-12, Vol.57 (6), p.3614-3622
Main Authors: Hayvaci, Harun Taha, Shahi, Maryam, Ilbegi, Handan, Yetik, Imam Samil
Format: Article
Language:English
Subjects:
Circuits
Classification
Electronic circuits
Electronic devices
estimator
Harmonic analysis
harmonic radar
Incident waves
Integrated circuit modeling
k-nearest neighbors
linear model
Mathematical models
Maximum likelihood estimators
Parameters
power series model
Power system harmonics
Radar
Radar detection
Radar tracking
Random noise
vector of parameters
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Summary:A new approach to exploit nonlinear reradiation of electronic circuits under test (ECUT) for classification of electronic devices using harmonic radar is proposed in this article. Unlike prior approaches, we develop a linear model to relate the measurements to the unknown parameters representing the nonlinear characteristics of the ECUT. Each nonlinear circuit under test has a distinguishable response to a power-varying single-tone or two-tone incident wave. As a result, each ECUT possesses a unique unknown deterministic vector of parameters derived from the proposed linear model. We use maximum likelihood estimator in the presence of complex white Gaussian noise based on the newly constructed linear model. The statistical features of the normalized estimated vectors of parameters are employed as distinguishing factors in classification of different nonlinear electronic devices using K-nearest neighbors classification method. Simulation results show that the proposed method of linear model with power-swept signals and estimated vectors of parameters successfully classifies nonlinear devices.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2021.3079572