Compressed sensing-based frequency selection for classification of ground penetrating radar signals

In this paper we present an automatic classification system for ground penetrating radar (GPR) signals. The system extracts the magnitude spectra at resonant frequencies and classifies them using support vector machines. To locate the resonant frequencies, we propose an approach based on compressed sensing and orthogonal matching pursuit. The performance of the system is evaluated by classifying GPR traces from different ballast fouling conditions. The experimental results show that the proposed approach, compared to the approach of using frequencies at local maxima, represents the GPR signal more efficiently using a small number of coefficients, and obtains higher classification accuracy.