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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.
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