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Application of Polarimetric Features and Support Vector Machines for Classification of Improvised Explosive Devices

In this letter, we present a binary classification problem of improvised explosive devices, where true and false target features are taken from polarimetric ground-penetrating radar (GPR) signals. The radar measurements are carried out in laboratory and field scenarios by using dual-polarized Vivaldi antennas and ultrawideband multiple-input–multiple-output GPR (MIMO-GPR) systems in the frequency range from 0.8 to 5 GHz. Recursive algorithms, recursive least square, and linear predictive coding are used for clutter removal. To extract targets features of polarimetric measurements, eight data-processing methods are assembled, combining clutter removal algorithms, time–frequency transformations, and singular value decomposition. Moreover, for every method, 13 target feature vectors are constructed. Each vector is used to train and test a support vector machine algorithm for the classification problem. Classification results are validated by using leave-two-out cross-validation. Accuracy of 87.02% and a false positive rate of 10.53% in the best classifier were obtained.