Statistical Modeling to Improve Buried Target Detection with a Forward-Looking Ground-Penetrating Rada

Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote sensing modality for buried target detection (e.g., landmines and improvised explosive devices (IEDs)). In this context, raw FLGPR data is commonly beamformed into images and then computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning they are trained to discriminate between labeled target and non-target imagery, usually based on features extracted from the radar imagery. This thesis is composed of two FLGPR research areas: an analysis of image features for classification, and the application of machine learning techniques to the formation process of radar imagery.;A large number of image features and classifiers have been proposed for detecting landmines in the FLGPR imagery, but it has been unclear which were the most effective. The primary goal of this component of my research is to provide a comprehensive comparison of detection performance using existing features on a large collection of FLGPR data. Fusion of the decisions resulting from processing each feature is also considered. These comparisons have not previously been performed, and a novel 2DFFT feature was also developed for the FLGPR application. Another contribution of my research in the image feature investigation was the analysis of two modern feature learning approaches from the object recognition literature: the bag-of-visual-words and the Fisher vector for FLGPR processing. The results indicate that most image classification algorithms perform similarly, though the newly designed 2DFFT-based feature consistently performs best for landmine detection with the FLGPR.;Based on the image feature results presented in this work, it appears that the current feature extractors are leveraging most of the information available in the radar images that are produced by the conventional beamforming process. The work presented in the second component of this thesis improves the beamforming process applied to the radar responses. By improving the radar images (i.e., increasing signal to noise ratio, or SNR), each feature extractor and classification algorithm is shown to subsequently increase in performance. These new methods are designed to incorporate multiple uncertainties in the physical world that are currently ignored during conventional beamforming. The two approaches to improving the underlying FLGPR image are a learned weighting applied to the antenna responses and a strategy for selecting the image creation depth. Both of these two new beamforming process approaches yield additional improvements to the imagery which are reflected in improved detection results.