Detecting landmines using weighted density distribution function features

摘要

Land mine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise due to: 1) the varying composition and type of metal in land mines, 2) the time-varying nature of background and 3) the variation in height and velocity of the hand-held unit in data measurement. This research introduces new spatially distributed MD features for differentiating land mine signatures from background. The spatially distributed features involve correlating sequences of MD energy values with six weighted density distribution functions. These features are evaluated using a standard back propagation neural network on real data sets containing more than 2,300 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions.