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<sup>-3</sup>/m<sup>2</sup>. A description of the test scenarios applied and performance results for
these scenarios are summarized in this report.
This project is aimed at analyzing EO/IR images to provide automatic target detection/recognition/identification (ATR/D/I) of militarily relevant land targets. An increase in performance was accomplished using a biomimetic intelligence system functioning on low-cost, commercially available processing chips. Biomimetic intelligence has demonstrated advanced capabilities in the areas of hand- printed character recognition, real-time detection/identification of multiple faces in full 3D perspectives in cluttered environments, advanced capabilities in classification of ground-based military vehicles from SAR, and real-time ATR/D/I of ground-based military vehicles from EO/IR/HRR data in cluttered environments. The investigation applied these tools to real data sets and examined the parameters such as the minimum resolution for target recognition, the effect of target size, rotation, line-of-sight changes, contrast, partial obscuring, background clutter etc. The results demonstrated a real-time ATR/D/I capability against a subset of militarily relevant land targets operating in a realistic scenario. Typical results on the initial EO/IR data indicate probabilities of correct classification of resolved targets to be greater than 95 percent.