Abstract
In this paper, a method for nonlinear target recognition via machine learning is presented. The nonlinear radar environment used in this study was a frequency-modulated continuous-wave (FMCW) nonlinear radar with a transmit frequency band of 3.0~3.2 GHz and received a frequency band of 6~6.4 GHz corresponding to the second harmonics. Nonlinear radar measurements were performed using four types of electronic devices as nonlinear targets. Statistical parameters were extracted from the measured amplitude spectrum of the received harmonic responses for each target to successfully construct a classification algorithm. The extracted characteristic data were then used to construct and verify a support vector machine (SVM) classifier. The accuracy of the target classification by the trained SVM classifier was confirmed through verification data, and an accuracy of 85 % with 10-fold cross-validation was demonstrated.
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