The detection of buried land mines in soil is a well-studied problem; many existing technologies are designed and
optimized for performance in different soil types. Research on mine detection in shallow water environments such as
beaches, however, is much less developed. Electrical impedance tomography (EIT) shows promise for this application.
EIT uses current-stimulating and voltage-recording electrode pairs to measure trans-impedances in the volume directly
beneath the electrode array, which sits flat over the ground surface. The trans-impedances are used to construct a
conductivity profile of the volume. Non-metallic and metallic explosives appear as perturbations in the conductivity
profile, and their location and size can be estimated. Lab testing has yielded promising results using a submerged array
positioned over a sand bed. The instrument has also successfully detected surrogate mines in a traditional soil
environment during field trials. Resolution of the detector is roughly half the pitch of electrodes in the array. In
underwater lab testing, non-conducting targets buried in the sand are detected at a depth of 1.5 times the electrode pitch
with the array positioned up to one electrode pitch above the sand bed. Results will be presented for metallic and non-metallic
targets of various shapes and sizes.
KEYWORDS: Target recognition, Automatic target recognition, 3D acquisition, Sensors, Databases, Detection and tracking algorithms, 3D modeling, Atomic force microscopy, 3D image processing, LIDAR
Automatic Target Recognition (ATR) using three-dimensional (3D) sensor data has proven very successful in
experimental platforms. One of the factors limiting the implementation of these approaches is lag in operational
hardware to provide the type of data required. Neptec has addressed this sensor concern in its 3D ATR software. The
need for specific operational 3D sensing hardware is avoided by using a generic range image format, and a shape-from-motion
(SfM) method enables the generation of 3D data using widely available 2D sensors.
The previously reported ATR software has been expanded from proof-of-concept ground-to-ground to include air-to-ground
capabilities. The system uses a generic 3D model of the target, such as from CAD or scanned from a scale or
full-sized model which does not need to be perfect. The rapid recognition approach simultaneously provides target pose
estimation. This capability has been demonstrated using ground-based imaging LiDAR, airborne LiDAR, scannerless
AMcw LiDAR, and shape-from-motion using a 2D camera. Multiple data sets can be fused to optimize confidence in
the recognition and provide measures of similarity between different targets and the data set.
This paper presents an overview of the 3D ATR approach and updates performance characteristics from a variety of
tests that include synthetic data, lab tests, and field tests. It is shown that the approach is fast, highly robust, flexible, and
is primarily limited by the quality of sensor data. Particular emphasis is placed on the shape-from-motion application
since this capability can make use of widely used operational 2D imaging sensor packages.
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