Leveraging robust principal component analysis to detect buried explosive threats in handheld ground-penetrating radar data

Abstract

A goal of ground penetrating radar (GPR) preprocessing is to distinguish background from data containing explosive threats. This is commonly achieved by performing depth-dependent mean and standard deviation normalization, where the mean and standard deviation are computed on background data. Under the assumption that data with explosive threats have different statistical characteristics than the background/clutter, after normalization explosive threat data will have larger absolute normalized scores than the background/clutter. An underlying problem is determining which data to compute the background mean and standard deviation statistics over. Often the background statistics are computed over a moving window, which is centered at the location of interest and has a predetermined guard band, a region of data that is ignored. However, buried explosive threats vary considerably in their shapes and more importantly sizes subsequently, the size of the GPR responses from these objects are considerably varied. We examine a number of additional detection methods that utilize Robust Principal Component Analysis (RPCA), where RPCA decomposes the data into low-rank and sparse components. Intuitively, the low-rank component should capture the background data and the sparse should capture the anomalous explosive threat response. We find that detection performance using energy- and shape-based detection algorithms improves when using RPCA preprocessing.