The identification followed by avoidance or removal of explosive hazards in past and/or present conflict zones is a serious threat for both civilian and military personnel. This is a challenging task as extreme variability exists with respect to the objects, their environment and emplacement context. A goal is the development of automatic, or human-in-the-loop, sensor technologies that leverage engineering theories like signal processing, data fusion and machine learning. Herein, we explore the detection of buried explosive hazards (BEHs) in handheld ground penetrating radar (HH-GPR) via convolutional neural networks (CNNs). In particular, we investigate the potential for generative adversarial networks (GANs) to impute new data based on limited and class imbalance labeled data. Unsupervised GANs are trained and assessed at a qualitative level and their outputs are explored in different ways to quantitatively help train a CNN classifier. Overall, we found encouraging qualitative results and a list of hurdles that need to be overcome before we anticipate quantitative improvements.
The identification followed by avoidance or removal of explosive hazards in past and/or present conflict zones is a serious threat for both civilian and military personnel. This is a challenging task as variability exists with respect to the objects, their environment and emplacement context, to name a few factors. A goal is the development of automatic or human-in-the-loop sensor technologies that leverage signal processing, data fusion and machine learning. Herein, we explore the detection of side attack explosive hazards (SAEHs) in three dimensional voxel space radar via different shallow and deep convolutional neural network (CNN) architectures. Dimensionality reduction is performed by using multiple projected images versus the raw three dimensional voxel data, which leads to noteworthy savings in input size and associated network hyperparameters. Last, we explore the accuracy and interpretation of solutions learned via random versus intelligent network weight initialization. Experiments are provided on a U.S. Army data set collected over different times, weather conditions, target types and concealments. Preliminary results indicate that deep learning can perform as good as, if not better, than a skilled domain expert, even in light of limited training data with a class imbalance.
Explosive hazards in current and former conflict zones are a serious threat to both civilians and soldiers alike. Significant effort has been dedicated to identifying sensors, algorithms and fusion strategies to detect such threats. However, a challenging aspect of the field is that we are not necessarily at war with the threats (objects). Instead, we are at conflict with people who are constantly evolving their strategies of attack along with their preferred threat. One such method of threat delivery is side attack explosive ballistics (SAEB). In this article, we explore different 3D voxel-space radar signal processing methods for SAEB detection on a U.S. Army provided vehicle-mounted platform. In particular, we explore the fusion of a matched filter (MF) and size contrast filter (SCF). Clustering is applied to the fused result and heuristics are used to reduce the systems false alarm rate. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site containing multiple target and clutter types, levels of concealment and times of day.
Explosive hazards in current and former conflict zones are a threat to both military and civilian personnel. As a result,
much effort has been dedicated to identifying automated algorithms and systems to detect these threats. However, robust
detection is complicated due to factors like the varied composition and anatomy of such hazards. In order to solve this
challenge, a number of platforms (vehicle-based, handheld, etc.) and sensors (infrared, ground penetrating radar, acoustics,
etc.) are being explored. In this article, we investigate the detection of side attack explosive ballistics via a vehicle-mounted
acoustic sensor. In particular, we explore three acoustic features, one in the time domain and two on synthetic aperture
acoustic (SAA) beamformed imagery. The idea is to exploit the varying acoustic frequency profile of a target due to its
unique geometry and material composition with respect to different viewing angles. The first two features build their
angle specific frequency information using a highly constrained subset of the signal data and the last feature builds its
frequency profile using all available signal data for a given region of interest (centered on the candidate target location).
Performance is assessed in the context of receiver operating characteristic (ROC) curves on cross-validation experiments
for data collected at a U.S. Army test site on different days with multiple target types and clutter. Our preliminary results
are encouraging and indicate that the top performing feature is the unrolled two dimensional discrete Fourier transform
(DFT) of SAA beamformed imagery.
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