Applying Statistics and Modeling to UXO Discrimination

Applying Statistics and Modeling to UXO Discrimination

Robust statistical methods and modeling techniques significantly improve DoD’s ability to distinguish UXO from harmless metal objects.

  • Dr. Stephen Billings, Sky Research, Inc. and University of British Columbia
    Robust Statistics and Regularization for Feature Extraction and UXO Discrimination

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 real risk reduction.

One of the keys to UXO classification is the ability to fit geophysical data into a model that accurately represents parameters of a physical object. Such parameters include the object’s length and shape, and the material it is made of. Complicating the task are real-world factors, such as surveying over uneven ground, that affect the quality of data that can be collected in the field.

Dr. Stephen Billings and his colleagues combined fundamental understanding of the underlying physics with their experience in the practicalities of gathering field data to develop robust statistical methods and modeling techniques for improving UXO classification and discrimination. These methods will improve parameter estimates and ultimately provide DoD with significant improvements in its ability to distinguish between UXO and harmless metal objects.

For this work, Dr. Billings received a Project-of-the-Year award at the annual Partners in Environmental Technology Technical Symposium & Workshop held November 30 – December 2, 2010, in Washington, D.C.

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