Data from multiple sensors has been collected using a handheld system, and includes precise location information. These sensors include ground penetrating radar (GPR) and electromagnetic induction (EMI) sensors. The performance of these sensors on different mine-types varies considerably. For example, the EMI sensor is effective at locating relatively small mines with metal while the GPR sensor is able to easily detect large plastic mines. In this work, we train linear (logistic regression) and non-linear (gradient boosting decision trees) methods on the EMI and GPR data in order to improve buried explosive threat detection performance.
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.
In this work, we explore the efficacy of two buried threat detectors on handheld data. The first algorithm is an energy-based algorithm, which computes how anomalous a given A-scan measurement after it is normalized according to its local statistics. It is based on a commonly used prescreener for the Husky Mounted Detection System (HMDS). In the HMDS setting measurements are sampled on a crosstrack-downtrack grid, and sequential measurements are at neighboring downtrack locations. In contrast, in the handheld setting sequential scans are often taken at neighboring crosstrack locations, and neighboring downtrack locations can be hundreds of scans away. In order to include both downtrack and crosstrack information, we compute local statistics over a much larger area than in the HMDS setting. The second algorithm is a shape-based algorithm. Shape Invariant Feature Transform (SIFT) features, which capture the gradient distributions of local patches, are extracted and used to train a non-linear Support Vector Machine (SVM). We found that in terms of AUC, the SIFT-SVM algorithm results in a 2.2% absolute improvement over the energy-based algorithm, with the greatest gains seen at lower false alarm rates.
A recently validated technique for buried target detection relies on applying an acoustic stimulus signal to a patch of earth and then measuring its seismic (vibrational) response using a laser Doppler vibrometer (LDV). Target detection in this modality often relies on estimating the acoustic-to-seismic coupling ratio (A/S ratio) of the ground, which is altered by the presence of a buried target. For this study, LDV measurements were collected over patches of earth under varying environmental conditions using a known stimulus. These observations are then used to estimate the performance of several methods to discriminate between target and non-target patches. The first part of the study compares the performance of human observers against a set of established seismo-acoustic features from the literature. The simple features are based on previous studies where statistics on the Fourier transform of the acoustic-to-seismic transfer function estimate are measured. The human observers generally offered much better detection performance than any established feature. One weakness of the Fourier features is their inability to utilize local spatiotemporal target cues. To address these weaknesses, a novel automatic detection algorithm is proposed which uses a multi-scale blob detector to identify suspicious regions in time and space. These suspicious spatiotemporal locations are then clustered and assigned a decision statistic based on the confidence and number of cluster members. This method is shown to improve performance over the established Fourier statistics, resulting in performance much closer to the human observers.
Recent research has shown that synthetic aperture acoustic (SAA) imaging may be useful for object identification. The goal of this work is to use SAA information to detect and identify four types of objects: jagged rocks, river rocks, small concave capped cylinders, and large concave capped cylinders. More specifically, we examine the use of frequency domain features extracted from the SAA images. We utilize Support Vector Machines (SVMs) for target detection, where an SVM is trained on target and non-target (background) examples for each target type. Assuming perfect target detection, we then compare multivariate Gaussian models for target identification. Experimental results show that SAA-based frequency domain features are able to detect and identify the four types of objects.
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