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- Using Plants to Sustain Military Ranges
- Sonar Key to Detecting Underwater UXO
- Monitoring and Mapping Coral Reefs
- EPA-Approved Protocol for Range Characterization
- Robotic Laser Coating Removal System
- Understanding cis-DCE and VC Biodegradation
- Eliminating Cr from Medium Caliber Gun Barrels
- Predicting Responses to Landscape Changes
- Applying Statistics and Modeling to UXO Discrimination
- Composites with Low HAP Compounds
- Perchlorate-Free Flares Undergo Qualification Testing
- Recovering Energy from Landfill Gas
- Modeling Underwater UXO Mobility in Reef Environments
- Understanding the Behavioral Ecology of Cetaceans
- Forecasting the Effects of Stressors on At-Risk Species
- Advanced Signal Processing for UXO Discrimination
- Reducing Emissions for Jet Engines of the Future
- Assessing Vapor Intrusion at Chlorinated Solvent Sites
- Passive Sampling of Contaminated Sediments
- Leveraging Advanced Sensor Data to Clean Up UXO
- Source Zone Architecture Key to DNAPL Remediation
- Biopolymers Maintain Training Berms, Prevent Contamination
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- Cold Spray Technology for Aircraft Component Repair
- Ecological Research Supports Training at Camp Lejeune
- Loss of Permafrost – Impact on DoD Lands in Alaska
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- ASETSDefense Workshop on Sustainable Surface Engineering
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- Evaluating Matrix Diffusion Effects on Groundwater
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- Climate Change Adaptation: Enhanced Decision Making
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- Workshop on Sustainable Surface Engineering Advances
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- Munitions Classification in the Hands of Production Firms
- Intelligent and Energy-Efficient LED Street Lighting
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- White House Energy Security Blueprint References ESTCP
- Success Classifying Munitions in Wooded Areas
- Evaluating Technology Performance at DNAPL Sites
- ‘Flyer’ Improves OB/OD Air Emissions Measurement
- Identifying Research Needs for Underwater Munitions
- Success Classifying Small Munitions at Camp Butner
- Managing Military Lands in the Southwest
- Partnering to Advance Munitions Classification
- ‘Flyer’ Improves OB/OD Air Emissions Measurement - Preview
- Sonar Identifies Underwater Munitions in Gulf Study
- Protective Coating Improves Jet Engine Fuel Efficiency
- Assessing Pacific Island Watershed Health
- New Insights Into Tracking Contaminants in Bedrock
- ClimaStat Technology Improves HVAC Efficiency
- Innovative Plating Process for Beryllium Alternatives
Advanced Signal Processing for UXO Discrimination
Sophisticated models applied to advanced EMI sensor data significantly improve the ability to distinguish UXO from clutter, reducing munitions response costs and accelerating the cleanup process.
Dr. Fridon Shubitidze, Dartmouth College and Sky Research, Inc.
A Complex Approach to UXO Discrimination: Combining Advanced EMI Forward and Statistical Signal Processing
DoD’s liability for munitions response is estimated in the tens of billions of dollars. With resources constrained, munitions response actions on many sites are forecast to be decades out. One of the most promising technology advances for reducing the cost per site and accelerating the pace of cleanup is in the use of classification to distinguish the buried unexploded ordnance (UXO) from the vast quantity of harmless pieces of metal found on any site, allowing resources to be directed to removing only the UXO.
Recently developed advanced electromagnetic induction (EMI) sensors record detailed responses from buried targets that have powerful classification potential. The traditional models used to analyze sensor data, however, are unable to exploit all the information available from these sensors.
Dr. Fridon Shubitidze and his colleagues developed sophisticated, physically complete models that extract more meaningful parameters from advanced sensor data for classification. Their methods are applicable to all currently available advanced electromagnetic sensors and easily extended to others that may be developed. These models have rapidly transitioned to field demonstration. In fact, Dr. Shubitidze and his team demonstrated near perfect classification at the former Camp Butner in North Carolina.
These new models will lead to significant improvements in the ability to distinguish between UXO and harmless objects, particularly on difficult sites. Using classification, substantial cost savings will be realized and available resources can be used to accelerate risk reduction on munitions response sites.
For this work, Dr. Shubitidze received a Project-of-the-Year award at the annual Partners in Environmental Technology Technical Symposium & Workshop held November 29 –December 1, 2011, in Washington, D.C.
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