Land mine detection applying holographic neural technology (HNeT)

Abstract

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.