In this work the singular value decomposition (SVD) is used to analyze matrices of ground penetrating radar (GPR) data. The targets to be detected are Russian PMN antipersonnel landmines and improvised explosive devices constructed from 155mm artillery shells. Target responses are simulated with GPRmax 2D, a simulation package based on the Finite- Difference-Time-Domain method. First, the utility of the SVD for image enhancement and reconstruction is demonstrated. Then the singular values and singular vectors of the decomposed matrices are analyzed with the goal of finding properties that will aid in the development of automated underground detection algorithms
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