Provided is a summary of Holographic Neural Technology (HNeT) and its application in detecting land mines using
airborne Synthetic Aperture Radar (SAR) imagery. Tests were performed for three surface mine classes (small
metallic, large metallic, and medium-sized plastic) located within variable indigenous background clutter (bare dirt,
short/tall grass). This work has been performed as part of the Wide Area Airborne Minefield Detection (WAAMD)
Program at the U. S. Army Night Vision Labs and Electronic Sensors Directorate in Fort Belvoir, VA. The ATR
algorithm applied was Holographic Neural Technology (HNeT); a neuromorphic model based upon non-linear phase
coherence/de-coherence principles. The HNeT technology provides rapid learning capabilities and an advanced
capability in learning and generalization of non-linear relationships. Described is a summary of the underlying HNeT
technology and the methodologies applied in the training of the neuromorphic system for mine detection using target
images (land mines) and back ground clutter images. Provided also is a summary description of the software tools
applied in the development of the mine detection capability.
Performance testing of the mine detection algorithm separated training and testing sensor image sets by airborne
sensor depression angle and surface ground condition indigenous to site location (Countermine Alpha, Yellow Sands).
Detection performance was compared in the analysis of complex versus magnitude sensor data. Performance results
from independent test imagery indicated a reasonable level of clutter rejection, providing > 50% probability of
detection at a false detection rate < 10-3/m2. A description of the test scenarios applied and performance results for
these scenarios are summarized in this report.
KEYWORDS: Synthetic aperture radar, Detection and tracking algorithms, Data modeling, Mining, Land mines, Target detection, X band, Bandpass filters, Magnetism, Analytical research
The Army Research Laboratory has recently collaborated with Raytheon to determine the effects of various target signature phenomena on the performance of a detection pre-screening processing chain. These signature phenomena for plastic mines were predicted by ARL's high fidelity electromagnetic models and then observed in airborne X-band synthetic aperture radar (SAR) data. The agreement of the modeled results with experimental data was then used to guide pre-screener design.
In this paper we present predicted plastic mine signatures generated by ARL and compare the results with actual target samples extracted from X-Band SAR data. We then briefly describe the new prescreener algorithm and examine modeling results for other frequency bands in an effort to determine if similar notions can be exploited in these bands as well.
Airborne automated target detection (ATD) and fusion experiments are frequently limited by the quality, quantity, and rapid availability of geo-registered multi-sensor, multi-platform imagery. This is especially true when working with mine targets that are smaller than the inertial measurement errors on airborne platforms. Working under the sponsorship of NVESD, we have developed and demonstrated an automated approach to inertially geo-register and ground truth imagery from multiple sensor modalities at accuracies on the order of an antitank mine dimension. Data types include ground penetrating, X-band, and Ku-band synthetic aperture radar, visible to near infrared (VIS/NIR) and longwave infrared (LWIR). This database is being used to support feature and decision-level sensor and algorithm fusion studies and to extract sensor utility metrics for a wide range of operational condition subspaces. In addition, we have standardized the format of the ground-truthed imagery products for dissemination to a larger algorithm development community and for compatibility with the U.S. Army Research Laboratory’s (ARL) Automatic Target Detection Evaluation Environment (ATD EvalEnv). This environment facilitates mine detection and fusion algorithm performance assessment across sensors, algorithms, and operational conditions. In this paper, we will discuss a process for fusion studies, including the ARL infrastructure and the techniques employed to collect and prepare the inertially co-registered imagery database.
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