Proc. SPIE. 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV
KEYWORDS: Target detection, Soil science, Detection and tracking algorithms, Sensors, Calibration, Surface roughness, Sensor performance, Environmental sensing, Land mines, General packet radio service
Algorithms developed for the detection of landmines are tasked with discriminating a wide variety of targets in a diverse array of environmental conditions. However, the potential performance of a detection algorithm may be underestimated by evaluating it in batch on a large, diverse dataset. This is because environmental, or in general, contextual, factors may contribute signiﬁcant variance to the output of a detection algorithm across diﬀerent contexts. One way to view this is as a problem of miscalibration: within each context, the output scores of a detection algorithm can be seen as miscalibrated relative to the scores produced in the other contexts. As a result of this miscalibration, the observed receiver operating characteristic (ROC) curve for a detector can have a sub-optimal area-under-the-curve (AUC). One solution, then, is to re-calibrate the detector within each context. In this work, we identify multiple sets of contexts in which diﬀerent landmine detection algorithms exhibit signiﬁcant output variance and, consequently, miscalibration. We then apply a monotonic calibration strategy that maximizes AUC and demonstrate the gain in observed performance that results when a landmine detection algorithm is properly calibrated within each context.
Ground penetrating radar (GPR) devices use sensors to capture one-dimensional representations, or A-scans, of the soil and buried properties at each sampling point. Previous work uses reciprocal pointer chains (RPCs) to find one-dimensional layers in two-dimensional data (B-scans). We extend this work to find two-dimensional layers in three-dimensional data. We explore the application and differences of our technique when applied to vehicular mounted systems versus handheld systems and their distinct detection sequences. Not only can this work be used to display subsurface structure to a system operator, but we can also use changes in the subsurface structure of a local region to help identify buried objects within the data. We propose distinguishing buried objects from layers can reduce false alarm rates and may help increase probability of detection.
The accurate detection of a diverse set of targets often requires the use of multiple sensor modalities and algorithms. Fusion approaches can be used to combine information from multiple sensors or detectors. But typical fusion approaches are not suitable when detectors do not operate on all of the same locations of interest, or when detectors are specialized to detect disjoint sets of target types. Run Packing is an algorithm we developed previously to optimally combine detectors when their output never coincides, which can be expected when the detectors are specialized to detect different target types. But when asynchronous detectors sometimes coincide, or specialized detectors sometimes detect the same target, Run Packing ignores this coincidence information and thus may be suboptimal in certain cases. In this paper, we show how multi-detector fusion involving partially coinciding alarms can be re-framed as an equivalent fusion problem that is optimally addressed by Run Packing. This amounts to a hierarchical or hybrid approach involving fusion methods to join coinciding alarms at the same location into a single unified alarm and then using Run Packing to optimally fuse the resulting set of non coinciding alarms. We report preliminary results of applying the method in a few typical landmine detection scenarios to demonstrate its potential utility.
The Run Packing (RP) fusion method is a novel algorithm that addresses the con dence level fusion problem when M different sensors (or alarm sources) produce alarms independently. The goal of such a fusion method is to map the output confidence range of each alarm source to a global range shared by all of the alarm sources. The shared global confidence range allows a single receiver operating characteristics (ROC) curve to be created, and this ROC then shows the global system performance trade-o s across all alarm sources. We explain the run packing algorithm, show its application to a multi-sensor buried explosive object detection system, and compare its performance to other fusion techniques.
In landmine detection using vehicle-mounted ground-penetrating radar (GPR) systems, ground tracking has
proven to be an eective pre-processing step. Identifying the ground can aid in the correction of distortions in
downtrack radar data, which can result in the reduction of false alarms due to ground anomalies. However, the
air-ground interface is not the only layer boundary detectable by GPR systems. Multiple layers can exist within
the ground, and these layers are of particular importance because they give rise to anomalous signatures below
the ground surface, where target signatures will typically reside. In this paper, an ecient method is proposed
for performing multiple ground layer-identication in GPR data. The method is an extension of the dynamic
programming-based Viterbi algorithm, nding not only the globally optimal path, which can be associated with
the ground surface, but also locally optimal paths that can be associated with distinct layer boundaries within
the ground. In contrast with the Viterbi algorithm, this extended method is uniquely suited to detecting not
only multiple layers that span the entire antenna array, but also layers that span only a subset of the channels
of the array. Furthermore, it is able to accomplish this while retaining the ecient nature of the original Viterbi
In using GPR images for landmine detection it is often useful to identify the air-ground interface in the GPR
signal for alignment purposes. A number of algorithms have been proposed to solve the air-ground interface
detection problem, including some which use only A-scan data, and others which track the ground in B-scans or
C-scans. Here we develop a framework for comparing these algorithms relative to one another and we examine
the results. The evaluations are performed on data that have been categorized in terms of features that make the
air-ground interface difficult to find or track. The data also have associated human selected ground locations,
from multiple evaluators, that can be used for determining correctness. A distribution is placed over each of the
human selected ground locations, with the sum of these distributions at the algorithm selected location used as
a measure of its correctness. Algorithms are also evaluated in terms of how they affect the false alarm and true
positive rates of mine detection algorithms that use ground aligned data.
In this paper, we propose a new method for performing ground-tracking using ground-penetrating radar (GPR).
Ground-tracking involves identifying the air-ground interface, which is usually the dominant feature in a radar
image but frequently is obscured or mimicked by other nearby elements. It is an important problem in landmine
detection using vehicle-mounted systems because antenna motion, caused by bumpy ground, can introduce
distortions in downtrack radar images, which ground-tracking makes it possible to correct. Because landmine
detection is performed in real-time, any algorithm for ground-tracking must be able to run quickly, prior to other,
more computationally expensive algorithms for detection. In this investigation, we first describe an efficient
algorithm, based on dynamic programming, that can be used in real-time for tracking the ground. We then
demonstrate its accuracy through a quantitative comparison with other proposed ground-tracking methods, and
a qualitative comparison showing that its ground-tracking is consistent with human observations in challenging
This paper considers the use of data from a wideband electromagnetic induction (EMI) sensor in a prescreener for a
landmine detection system employing both ground-penetrating radar (GPR) and EMI sensors. The paper looks at a
unique EMI prescreening strategy based on the use of prototypes derived from a training set of landmines. We show
that this prescreener is robust to a wide range of induced energy levels in sensed objects. We also compare properties of
the receiver operating characteristics (ROC) curve of this prescreener on a varied collection of targets to the properties
of a GPR prescreener, identifying performance difference with respect to target object classes.