Application of Markov random fields to landmine discrimination in ground penetrating radar data

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.