Browsing by Subject "ground penetrating radar"

Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has been investigated for landmine and subsurface threat detection. The radar is attached to front of a vehicle and collects measurements on the path of travel. At each spatial location queried, a time-series of measurements is collected, and then the measured set of data are often visualized as images within which the signals corresponding to buried threats exhibit a characteristic appearance. This appearance is typically hyperbolic and has been leveraged to develop several automated detection methods. Many of the detection methods applied to this task are supervised, and therefore require labeled examples of threat and non-threat data for training. Labeled examples are typically obtained by collecting data over deliberately buried threats at known spatial locations. However, uncertainty exists with regards to the temporal locations in depth at which the buried threat signal exists in the imagery. This uncertainty is an impediment to obtaining labeled examples of buried threats to provide to the supervised learning model. The focus of this dissertation is on overcoming the problem of identifying training data for supervised learning models for GPR buried threat detection.

The ultimate goal is to be able to apply the lessons learned in order to improve the performance of buried threat detectors. Therefore, a particular focus of this dissertation is to understand the implications of particular data selection strategies, and to develop principled general strategies for selecting the best approaches. This is done by identifying three factors that are typically considered in the literature with regards to this problem. Experiments are conducted to understand the impact of these factors on detection performance. The outcome of these experiments provided several insights about the data that can help guide the future development of automated buried threat detectors.

The first set of experiments suggest that a substantial number of threat signatures are neither hyperbolic nor regular in their appearance. These insights motivated the development of a novel buried threat detector that improves over the state-of-the-art benchmark algorithms on a large collection of data. In addition, this newly developed algorithm exhibits improved characteristics of robustness over those algorithms. The second set of experiments suggest that automating the selection of data corresponding to the buried threats is possible and can be used to replace manually designed methods for this task.

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.

The ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that have been investigated for buried target detection (BTD). GPR offers excellent detection performance, however, it is limited by a low rate of advance (ROA) due to its short sensing standoff distance. Standoff distance refers to the distance between the sensing platform and the location in front of the platform where the GPR senses the ground. Large standoff (high ROA) sensing modalities have been investigated as alternatives to the GPR but they do not (yet) achieve comparable detection performance. Another strategy to improve the ROA of the GPR is to combine it with a large standoff sensor within the same BTD system, and to leverage the benefits of the respective modalities. This work investigates both of the aforementioned approaches to improve the ROA of GPR systems using statistical modeling techniques. The first part of the work investigates two large-standoff modalities for BTD systems. New detection algorithms are proposed in both cases with the goal of improving their detection performance so that it is more comparable with the GPR. The second part of the work investigates two methods of combining the GPR with a large standoff modality in order to yield a system with greater ROA, but similar target detection performance. All proposed statistical modeling approaches in this work are tested for efficacy using real field-collected data from BTD systems. The experimental results show that each of the proposed methods contribute towards the goal of improving the ROA of BTD systems.