skip to main content
10.1145/1276958.1277353acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Discrimination of munitions and explosives of concern at F.E. warren afb using linear genetic programming

Published: 07 July 2007 Publication History

Abstract

Removing underground, unexploded bombs, mortars, cannon-shells and other ordnance ("MEC" or "UXO") from former military ranges is difficult and expensive. The principal difficulty is discriminating intact, underground ordnance from other metallic items such as fragments of exploded ordnance ("Clutter"), magnetic rocks, and "historic" items such as horseshoes, barbed-wire, and refrigerators. This study represents the first, large-scale, blind-test of MEC discrimination technology on production-grade, survey-mode data from the cleanup of a real impact site. The results reported here significantly advance the state-of-the-art in MEC discrimination over alternative forward-modeling/inversion approaches to performing MEC discrimination. We combined Linear Genetic Programming (LGP) and statistical analysis to process data from the cleanup of 600 acres of the F.E.Warren Air Force Base. That data contained almost 30,000 targets of interest identified by geophysicists, including three-hundred thirty-two 75mm projectiles (75mm) and 37mm projectiles (37mm). A little under one-third of the groundtruth was held back by the customer for blind-testing. Our task was to discriminate intact 37mm's and 75mm's from the clutter by ordering the targets from most-likely to be MEC to least-likely to be MEC in what is referred to as a "prioritized dig-list". We identified all 75mm's by 28.2% of the way through our prioritized dig-list and all 37mm's by 64.2% of the way through the prioritized dig list. Thus, depending on ordnance type, we reduced the number of targets that had to be excavated (false alarms) to clear the entire site by between 35% and 72%.

References

[1]
Banks, R. E., Nunez, E., Agarwal, P., Owens, C., McBride, M., and Liedel, R., 2005. Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO)). Late breaking paper at The Genetic and Evolutionary Computation Conference (GECCO-2005)
[2]
Banzhaf, W., Nordin, P. Keller, R. Francone, F. Genetic Programming, an Introduction, Morgan Kaufman Publishers, Inc., San Francisco, CA (1998).
[3]
Billings, L.R., and Oldenburg, D. W. (2003). Joint and Cooperative Inversion of Magnetic and Time Domain Electromagnetic Data for the Characterization of UXO, Proceedings of the Symposium on Application of Geophysics to Environmental and Engineering Problems 2003 (CD), Environmental and Engineering Geophysical Society, San Antonio, TX, 2003.
[4]
Billings, S. D., Pasion, L. R., and Oldenburg, D. W. (2003). Discrimination and Classification of UXO Using Magnetometry: Inversion and Error Analysis Using Robust Statistics, Proceedings of the Symposium on Application of Geophysics to Environmental and Engineering Problems 2003 (CD), E.E.G.S., San Antonio, TX, 2003.
[5]
Cespedes, E.: Advanced UXO Detection/Discrimination Technology Demonstration U.S. Army Jefferson Proving Ground, Madison, Indiana. US ACOE, Engineer Research and Development Center, ERDC/EL TR--01--20 (2001).
[6]
Defense Science Board Task Force. Report of the Defense Science Board Task Force on Unexploded Ordnance. Department of Defense. December (2003).
[7]
Deschaine, L. M., Hoover, R. A., Skibinski, J. N., Patel, J. J., Francone, F. D., Nordin, P. and Ades, M. J.: Using Machine Learning to Compliment and Extend the Accuracy of UXO Discrimination Beyond the Best Reported Results of the Jefferson Proving Ground Technology Demonstration. In: Proceedings of the Society for Modeling and Simulation International's Advanced Technology Simulation Conference, April 2002. San Diego, CA, USA (2002).
[8]
Environmental Security Technology Certification Program. Multi-Sensor Towed Array Detection System, Cost and Performance Report (UX--9526). September 1999.
[9]
Environmental Security Technology Certification Program. Environmental Induction and Magnetic Sensor Fusion for Enhanced UXO Target Classification, Cost and Performance Report (UX-9812). February 2004.
[10]
Francone, F. D., Discipulus Owner's Manual. RML Technologies, Inc. (2002). Available at www.aimlearning.com.
[11]
Francone, F. D., and Deschaine, L.M., Extending the Boundaries of Design Optimization by Integrating Fast Optimization Techniques with Machine-Code-Based Linear Genetic Programming, Information Sciences Journal-Informatics and Computer Science, Elsevier Press, Vol. 161/3--4 pp 99--120: 2004. Amsterdam, the Netherlands.
[12]
Francone, F. D., Deschaine, L. M., Battenhouse, T., and Warren, J. J., 2004. Discrimination of Unexploded Ordnance from Clutter Using Linear Genetic Programming, Proceedings of the Genetic and Evolutionary Computation Conference, Late Breaking Papers, 2004, Seattle, WA, USA.
[13]
Hanley, J. and McNeil, B., the Meaning and Use of the Area under a Receiver Operator Characteristic (ROC) Curve," Radiology, Vol. 143, pp. 29--36, 1982.
[14]
Jolliffe, I.T. Principal Components Analysis, Second Edition. (Springer Series in Statistics, NY, NY 2002), p. 1.
[15]
Nelson, H. H., Altshuler, T., Rosen, E., McDonald, J.R., Barrow, B., and Khadr, N. Magnetic Modeling of UXO and UXO--Like Targets and Comparison with Signatures Measured by MTADS, www.citeseer.ist.psu.edu/273329.html
[16]
Nordin, P., Francone, F. Banzhaf, W. Efficient Evolution of Machine Code for CISC Architectures Using Blocks And Homologous Crossover. In: Advances in Genetic Programming 3, MIT Press, Cambridge, MA (1998).
[17]
Pinter, J., Global Optimization in Action. Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications. Kluwer Academic Publishers, Dordrecht / Boston / London, 1996.
[18]
Tantum, S., Yu, Y., Zhu, Q., Wang, Y., and Collins, L. http://www.ee.duke.edu/research/collins/uxodocs.html, March 28, 2006

Cited By

View all
  • (2008)Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and ApplicationsComputational Intelligence: A Compendium10.1007/978-3-540-78293-3_22(927-1028)Online publication date: 2008

Index Terms

  1. Discrimination of munitions and explosives of concern at F.E. warren afb using linear genetic programming

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. EM61 MK2
    2. MEC
    3. UXO
    4. discipulus
    5. geophysics
    6. linear genetic programming
    7. munitions and explosives of concern
    8. unexploded ordnance

    Qualifiers

    • Article

    Conference

    GECCO07
    Sponsor:

    Acceptance Rates

    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 30 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2008)Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and ApplicationsComputational Intelligence: A Compendium10.1007/978-3-540-78293-3_22(927-1028)Online publication date: 2008

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media