Independent component analysis for UXO detection in highly cluttered environments

Abstract

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 measured sensor data. 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 independent component analysis (ICA), a well-established blind source separation (BSS) technique, to recover the unobserved object signatures from the mixed measurement data obtained by simulating electromagnetic induction (EMI) sensor data, 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, the separation distance between the UXO and clutter, and the number of mixed measurements available. Simulation results are presented illustrating the impact of these factors on discrimination performance.