Proc. SPIE. 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII
KEYWORDS: Detection and tracking algorithms, Scattering, Sensors, Feature extraction, Signal processing, Mining, Algorithm development, Electromagnetic coupling, Land mines, General packet radio service
The Region Processing Algorithm (RPA) has been developed by the Office of the Army Humanitarian Demining Research and Development (HD R&D) Program as part of improvements for the AN/PSS-14. The effort was a collaboration between the HD R&D Program, L-3 Communication CyTerra Corporation, University of Florida, Duke University and University of Missouri. RPA has been integrated into and implemented in a real-time AN/PSS-14. The subject unit was used to collect data and tested for its performance at three Army test sites within the United States of America. This paper describes the status of the technology and its recent test results.
The AN/PSS-14 (a.k.a. HSTAMIDS) has been tested for its performance in South East Asia (Thailand), South Africa (Namibia) and in November of 2005 in South West Asia (Afghanistan). The system has been proven effective in manual demining particularly in discriminating indigenous, metallic artifacts in the minefields. The Humanitarian Demining Research and Development (HD R&D) Program has sought to further improve the system to address specific needs in several areas. One particular area of these improvement efforts is the development of a mine detection/discrimination improvement software algorithm called Region Processing (RP). RP is an innovative technique in processing and is designed to work on a set of data acquired in a unique sweep pattern over a region-of-interest (ROI). The RP team is a joint effort consisting of three universities (University of Florida, University of Missouri, and Duke University), but is currently being led by the University of Florida. This paper describes the state-of-the-art Region Processing algorithm, its implementation into the current HSTAMIDS system, and its most recent test results.
The Humanitarian Demining Research and Development Program of the US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD), under the direction of the Office of Assistant Secretary of Defense for Special Operations and Low-Intensity Conflict (OASD SO/LIC) and with participation from the International Test and Evaluation Program (ITEP) for humanitarian demining, conducted an in-country field evaluation of the Handheld Standoff Mine Detection System (HSTAMIDS) in the southern African country of Namibia. Participants included the US Humanitarian Demining Team of NVESD; ITEP personnel from several member countries; deminers from two non-governmental organizations in Angola, Menschen Gegen Minen (MgM) and HALO Trust; and CyTerra Corporation. The primary objectives were to demonstrate the performance of the U.S. Army's newest handheld multisensor mine detector, the HSTAMIDS, to the performance of the metal detector being used by local demining organizations and also to assess the performance of deminers using the HSTAMIDS after limited experience and training.
The Humanitarian Demining Research and Development Program of Night Vision and Electronic Sensors Directorate (NVESD), under the direction of the Office of Assistant Secretary of Defense for Special Operations and Low-Intensity Conflict (OASD/SOLIC) and with participation from the International Test and Evaluation Project (ITEP) for Humanitarian Demining, conducted an in-country field evaluation of HSTAMIDS in the region of Humanitarian Demining Unit #1 (HMAU1) in Thailand. Participants included the US Humanitarian Demining Team of NVESD, ITEP personnel, Thailand Mine Action Center (TMAC), HALO Trust organization from Cambodia, and CyTerra Corporation. The primary objectives were to demonstrate the performance of the U.S. Army's latest handheld multisensor mine detector, the AN/PSS-14, in a demining environment in comparison to the performance of the metal detector being used by the local deminers and also to assess the performance of the trained deminers after limited experience and training with the HSTAMIDS.
Data collections were conducted using the AN/PSS-14 mine detector on three beach areas in Florida. A few samples of inert anti-tank (AT) and anti-personnel (AP) mines were buried at Jacksonville Beach, Cocoa Beach, and Clearwater Beach. The mines were buried in a variety of sand conditions varying from dry to saturated. The saturated sand conditions included the surf zone with up to two feet of water surge over the buried mine area. Test results indicate a good probability of detection (Pd) of all the buried mines by the AN/PSS-14 Ground Penetration Radar (GPR) and Metal Detector (MD), with a low false alarm rate. This paper will detail test conditions under which the mines were buried, soil dielectric and attenuation parameters measured versus water content in each condition, and interpretation of data in such highly attenuated (400-600 dB attenuation per meter) and extremely conductive soil. In addition, the theory of evanescent electromagnetic waves will be discussed in terms of the performance.
Sensor data fusion techniques are currently being investigated for potential applications in Handheld Standoff Mine Detection System (HSTAMIDS). Overall HSTAMIDS's performance must be optimized and improved over individual sensor's performance. In addition, HSTAMIDS's performance must be carefully traded off with hardware complexity, packaging challenges, increased cost associated with the sensor fusion process and operational requirements in support of military combat missions.
Infrared imagery scenes change continuously with environmental conditions. Strategic targets embedded in them are often difficult to be identified with the naked eye. An IR sensor-based mine detector must include Automatic Target Recognition (ATR) to detect and extract land mines from IR scenes. In the course of the ATR development process, mine signature data were collected using a commercial 8-12 (mu) spectral range FLIR, model Inframetrics 445L, and a commercial 3-5 (mu) starting focal planar array FLIR, model Infracam. These sensors were customized to the required field-of-view for short range operation. These baseline data were then input into a specialized parallel processor on which the mine detection algorithm is developed and trained. The ATR is feature-based and consists of several subprocesses to progress from raw input IR imagery to a neural network classifier for final nomination of the targets. Initially, image enhancement is used to remove noise and sensor artifact. Three preprocessing techniques, namely model-based segmentation, multi-element prescreener, and geon detector are then applied to extract specific features of the targets and to reject all objects that do not resemble mines. Finally, to further reduce the false alarm rate, the extracted features are presented to the neural network classifier. Depending on the operational circumstances, one of three neural network techniques will be adopted; back propagation, supervised real-time learning, or unsupervised real-time learning. The Close Range IR Mine Detection System is an Army program currently being experimentally developed to be demonstrated in the Army's Advanced Technology Demonstration in FY95. The ATR resulting from this program will be integrated in the 21st Century Land Warrior program in which the mine avoidance capability is its primary interest.