Global Optimization Toolbox Plays Significant Role in Solving
a Global Challenge
(UXO) location and discrimination struggle could finally
get a remedy
a paper submitted at the 2006 INFORMS Annual Conference,
Larry M. Deschaine presented an approach to solving the
challenge of accurately locating and discriminating unexploded
ordnance (explosives). He, along with colleagues Frank D.
Francone and János D. Pintér, has developed
a solution that fuses knowledge from Physics, Genetic Programming
and Global Optimization. Maplesoft’s Global Optimization
Toolbox is an important part of this solution.
A report by the Defense Science Board Task Force on Unexploded
Ordnance, Department of Defense, laid out the problem in
December 2003 as follows. “The UXO clean-up problem
is a very large-scale undertaking involving 10 million acres
of land at some 1400 sites. One of the key problems is …
[that] instruments that can detect the buried UXOs also
detect numerous scrap metal objects and other artifacts,
which leads to an enormous amount of expensive digging.
Typically 100 holes may be dug before a real UXO is unearthed.”
Though discriminating non-ordnance from
ordnance is a very important step, in many instances the
desired performance level is not reached. During a published
test sponsored by the US Government at the Jefferson Proving
Grounds, all contractors fell below the accuracy criteria,
and most solutions were hardly better than random guessing.
Larry and his colleagues set out to remove the inefficiencies
in UXO detection through tools that provably locate and
identify UXOs with high accuracy. Their solution also determines
when a UXO field is cleared, thus eliminating the issue
of digging up unnecessary holes. The UXO challenge is solved
– but only after a 7-year research and development
project that first started as a PhD dissertation topic at
Chalmers University of Technology (Göteborg, Sweden)
expanded to a production tool at SAIC and RMLT. The project
was recently validated on blind field data at a large UXO
project at FE Warren Air Force Base, Wyoming, USA.
The next phase of locating and identifying dangerous subsurface
objects is the detection of landmines. Millions of landmines
are buried in more than 80 countries, which result in 20,000
civilian victims every year. The landmine detection project
is in the R&D phase and Maplesoft software is providing
key value in this project as well.
The proposed solution is a subsurface object location and
identification method using a technique for modeling and
identifying objects on the basis of noisy, sparse data.
The solution approach fuses physics, data, and human expertise
models, and determines best algorithms for each challenge.
The techniques used include information theory, physics
based modeling, signal processing cognitive modeling, signal
filtering and global optimization. The challenge was to
integrate these techniques, and to determine the key information
content, in order to extend the accuracy of predictive modeling.
The solution is based on the fundamental geo-electromagnetic
equations and involves the following steps:
- Solid understanding of the physics;
derivation and key assumptions in the mathematical equations
- Steady and dynamic signal interpretation
- Insights from human expertise
- Development of optimal predictions
via an extended approach inspired by Kalman filtering
- Fusing the information in the analysis
to create the decision support tool UXO MineFinder (UXOMF™)
of Maple and the Global Optimization Toolbox from Maplesoft
Optimization was chosen to fit a model of the expected physics
of the UXO or non-UXO response as observed in the geo-electromagnetic
data. The model calibration problems were solved by the
seamlessly integrated global + local optimization software
suite LGO. LGO was chosen after an exhaustive search and
testing of available optimization algorithms. This powerful
optimization package is implemented for Maple (Maplesoft’s
flagship product) as the Global Optimization Toolbox. Maple
and the Global Optimization Toolbox (GOT) were selected
for their depth of mathematical and scientific resources,
and ease of use. Maple and its extensions (such as the GOT)
enable the efficient development and preservation of knowledge.
The project outcome was outstanding. Performance (detection
accuracy and reliability) results were better than all earlier
published results. It was established that UXOMF™
provides highest accuracy of UXO discrimination, and its
usage has led to a reduced need to dig test holes. Overall,
the results reduced excavation costs by at least 30-50%.
The current toolkit will be further refined, extended and
improved as the data, physics or subject matter expert analysis-models
improve, leading to better and more accurate results. This
includes ongoing R&D for the analysis and resolution
of both landmine and underwater UXO challenges.
is a leading provider of high-performance software tools
for engineering, science, and mathematics
provides advanced nonlinear optimization software products
is the developer of the Discipulus linear genetic programming
is a leading systems, solutions and technical services company.