Uxo discrimination using blind source separation

Statistical signal processing techniques have shown progress in discriminating UXO from clutter when the objects occur in isolation. Under this condition, only a single object contributes to the sensor measurement. For multiple closely-spaced subsurface objects, however, the unprocessed sensor measurement is a mixture of the responses from several objects. Consequently, the unprocessed measurements cannot be used directly to discriminate UXO from clutter. In this paper, we implement blind source separation (BSS) techniques, specifically independent component analysis (ICA), to recover the unobserved object signatures from the mixed measurement data obtained by electromagnetic induction (EMI) sensors, and then use the recovered signatures for UXO/clutter discrimination. Discrimination performance depends on multiple factors, including the number of clutter objects in proximity to the UXO and the separation distance between the UXO and clutter. Simulation results are presented illustrating the impact of these factors on discrimination performance.