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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