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Recent advances in ground penetrating radar (GPR) design and fabrication have resulted in improved fidelity
responses from relatively small, shallow-buried objects like landmines and improvised explosive devices. As the
responses measured with GPR improve, more and more advanced processing techniques can be brought to bear
on the problem of target identification in GPR data. From an electromagnetic point of view, the problem of
target detection in GPR signal processing is reducible to inferring the presence or absence of changes in the
electromagnetic properties of soils and thus the presence or absence of buried targets. Problems arise because
the algorithms required for the full electromagnetic inversion of GPR signals are extremely computationally
expensive, and usually rely on assumptions of electromagnetically constant transmission media; these problems
typically make the real-time implementation of purely electromagnetic-inspired algorithms infeasible. On the
other hand, purely statistical or signal-processing inspired approaches to target identification in GPR often lack a
solid theoretical basis in the underlying physics, which is fundamental to understanding responses in GPR. In this
work, we propose a model for responses in time-domain ground penetrating radar that attempts to incorporate
the underlying physics of the problem, but avoids several of the issues inherent in assuming constant media with
known electrical parameters by imposing a statistical model over the observed parameters of interest in A-scans - namely the signal gains, times of arrival, etc. The spatial requirements of the proposed statistical model suggests
the application of Markov random field (MRF) distributions which provide expressive, but computationally
simple models of spatial interactions. In this work we will explore the application of physics-based MRF's
as generative models for time-domain GPR data, the pre-screening algorithms that this model motivates, and
discuss how the model can be extended to other applications in GPR processing. Preliminary results showing
how the MRF approach to understanding the underlying physics can improve performance are also shown.
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