Signal processing of ground-penetrating radar data for subsurface object detection

Abstract

Ground penetrating radar (GPR) generates a cross-sectional profile of the soil by transmitting electromagnetic waves that reflect back in a manner associated with the electrical properties and geometry of the objects buried underground. The responses of the reflected waves are processed using a variety of digital signal processing and image processing techniques. In this paper we compare an energy detector, matched filter, and a proposed Hough Transform approach. The results from each of the algorithms are compared using receiver operating characteristic (ROC) curves. Comparatively, the matched filter method has the lowest false alarm rate, however it is essentially providing a performance bound since for this analysis we derived the matching template from the data to be tested. Thus, in this case the Hough transform method may be more robust when the testing and training sets are separate, as it is inherently integrating over the uncertainty associated with the subsurface object detection problem.